Formatted Doc Prasun Fyp Till Chap 9 ie6j

Automatic Door Lock System Using Facial Recognition in MATLAB and RFID

CERTIFICATE

This is to be certified that the following under mentioned student of Asia Pacific Institute of Information Technology has successfully completed the project Android – A True City Guide Friend. He has maintained all guidelines set by “STAFFORDSHIRE UNIVERSITY”. This project has been submitted in the given span of time. He has adopted no foul means to complete his project. I hereby declare that I have been handed over the project along with all necessary documents. I wish them good luck in all their future projects.

Student Name: Prasun Sinha Supervisor name: Mr. Pradeep Singhla Advisor’s name: Mr. Rinku Dhiman

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Asia Pacific Institute of Information Technology

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ACKONWLEDGEMENT I would like to express the deepest appreciation to my Head of Department Mr. Pradeep Singhla, who has shown the attitude and the substance of a genius: he continually and persuasively conveyed a spirit of adventure in regard to research and scholarship, and an excitement in regard to teaching. Without his supervision and constant help this dissertation would not have been possible. I would extend my deepest gratitude towards my supervisor Mr. Pradeep Singhla whose contribution in stimulating suggestions and encouragement, helped me to coordinate my project especially in writing this report. I am sincerely grateful to him for sharing his truthful and illuminating views on a number of issues related to the project. I would also like to thank my department lecturers whose work demonstrated to me that concern for global affairs ed by an ‘engagement’ in comparative literature and modern technology, should always transcend academia and provide a quest for our times. His time given to me is highly obliged. Without his help and demonstrations, this project would not have been completed on time. Furthermore, I would also like to acknowledge with much appreciation the crucial role of the staff of my college, who gave the permission to use all required equipment and the necessary materials to complete the project. Also, I would like to extend my thanks to the participants in my survey, who have willingly shared their precious time and knowledge during the process of interviewing and discussions. Last but never the least, I would like to thank my parents and friends who have ed me throughout the entire process, both by keeping me harmonious and helping me putting pieces together. I will be grateful forever.

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ABSTRACT The project aims at developing a system that would be able to recognize the face and hence open the door automatically another feature that has been in incorporated in the project is of RFID which uses a card which would also be required while accessing the automated door. The project shows the usage of an electronic gadget in which a software is programmed, the usage of such kinds of product have increased many folds in the past decade because of the advancement in electronic technology as well as in the field of software development. Now a day, due to rising security concerns and hectic life one needs to monitor the person entering in its home, offices, protect its important belongings or to safeguard itself from the day to day increasing crime thus making the project a much-needed development in today’s world. Making project do need proper project planning, it one of the most important step in a project as it involves setting achievable goals and achieving it within a given time frame. Here in the project Gantt chart and Pert chart were used in the process of planning the project. Then the developer did secondary research which involves developing a background for the topic. The research was done by collecting and analysing the research paper, a proper literature review was written in order to analyse the methods that had been incorporated early for building similar systems. Further in secondary research various resources like journals, magazines, books and website were studied. Then the developer did primary research which was in order to see the opinions of the realworld people whether they are in the favour of building such a system or not. The methods used while doing the research was formulating questionnaire, doing interview and focus group study. Then developer had to start the process of deg the system, in this process firstly the flow chart and block diagram of the system were designed then the research looked for similar kinds of product available in the market and the components used in fabricating the product. This helped it to decide the component which is going to be used. As the system as had to develop a program in the project so it started studying various algorithm and methods that it can implement in the process of programming. The developer had chosen MATLAB as its programming tool. Then it had to design a circuit in the simulation software, Proteus was chosen this task. Finally, the process and implementation started which ended with the testing of software as well as hardware.

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Then developer analysed the results it got from the testing and concluded it. It also gave some suggestion and that would act as the future enhancement of the project for the process of further implementation.

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Table of content List of Figure List of Tables

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CHAPTER 1 INTRODUCTION "Face Recognition" is a very active area in the MATLAB and Biometrics fields, as it has been studied vigorously for 25 years and is finally producing applications in security, robotics, human-computer-interfaces, digital cameras, games and entertainment. These types of systems have been grabbing high attention from commercial market point of view as well as pattern recognition field. It has received substantial attention from researches in biometrics, pattern recognition field and in MATLAB communities. The system can extract the features of face and compare this with the existing database. The faces considered here for comparison are still faces. Machine recognition of faces from still and video images is emerging as an active research area. The project is formulated based on video images captured by a web cam. The system detects only the faces from the image scene, extracts the descriptive features. It later compares with the database of faces, which is collection of faces in different poses. The present system is trained with the database, where the images are taken in different poses, with glasses, with and without beard. Humans are very good at recognizing faces and complex patterns. Even a age of time doesn't effect this capability and therefore it would help if computers become as robust as humans in face recognition. Face recognition system can help in many ways: 1. Checking for criminal records. 2. Enhancement of security by using surveillance cameras in conjunction with face recognition system. 3. Finding lost children's by using the images received from the cameras fitted at some public places. 4. Knowing in advance if some VIP is entering the hotel. 5. Detection of a criminal at public place. 6. Can be used in different areas of science for comparing an entity with a set of entities. 7. Pattern Recognition. This project is a step towards developing a face recognition system which can recognize static images. It can be modified to work with dynamic images. In that case, the dynamic images received from the camera can first be converted in to the static one's and then the same procedure can be applied on them. But then there are lots of other things that should be considered. Like distance between the camera and the person, magnification factor, view [top, side, front] etc. (jordon, 2017)

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"Face Recognition" generally involves two stages: 1. Face Detection, where a photo is searched to find any face, then image processing cleans up the facial image for easier recognition. 2. Face Recognition, where that detected and processed face is compared to a database of known faces, to decide who that person is.

Figure 1 Face Recognition Processing Flow (Ali Tarhini on software development and algorithms, 2017)

In figure 1 the face recognition processing flow is shown. The figure tells us about the general flow of the algorithms that are used for deg face recognition system. Firstly, through the image or video input face has to be detected then the face is aligned through features like face location, size and pose. The aligned face is the sent for feature extraction where the features are converted into feature vector. Now, with the help of feature vector the process of feature matching is done where the database of enrolled s is used for matching. If the system finds the right match the face is hence identified. TOPIC BACKGROUND Biometrics is used in the process of authentication of a person by ing or identifying that a requesting a network resource is who he, she, or it claims to be, and vice versa. It uses the property that a human trait associated with a person itself like structure of finger, face details etc. By comparing the existing data with the incoming data, we can the identity of a particular person. There are many types of biometric system like fingerprint recognition, face detection and recognition, iris recognition etc., these traits are used for human identification in surveillance system, criminal identification. Advantages of using these traits for identification are that they cannot be forgotten or lost. These are unique features of a human being which is being used widely. Biometric-based techniques have emerged as the most promising option for recognizing individuals in recent years since, instead of authenticating people and granting them access to

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physical and virtual domains based on s, PINs, smart cards, plastic cards, tokens, keys and so forth, these methods examine an individual’s physiological and behavioral characteristics in order to determine and ascertain his/her identity. s and PINs are hard to and can be stolen or guessed; cards, tokens, keys and the like can be misplaced, forgotten, purloined or duplicated; magnetic cards can become corrupted and unreadable. However, an individual’s biological traits cannot be misplaced, forgotten, stolen or forged. (Jafri & Arabnia, 2009) Biometric-based technologies include identification based on physiological characteristics (such as face, fingerprints, finger geometry, hand geometry, hand veins, palm, iris, retina, ear and voice) and behavioral traits (such as gait, signature and keystroke dynamics). Face recognition appears to offer several advantages over other biometric methods, a few of which are outlined here: Almost all these technologies require some voluntary action by the , i.e., the needs to place his hand on a hand-rest for fingerprinting or hand geometry detection and has to stand in a fixed position in front of a camera for iris or retina identification. However, face recognition can be done ively without any explicit action or participation on the part of the since face images can be acquired from a distance by a camera. This is particularly beneficial for security and surveillance purposes. Furthermore, data acquisition in general is fraught with problems for other biometrics techniques that rely on hands and fingers can be rendered useless if the epidermis tissue is damaged in some way (i.e., bruised or cracked). Iris and retina identification require expensive equipment and are much too sensitive to any body motion. Voice recognition is susceptible to background noises in public places and auditory fluctuations on a phone line or tape recording. Signatures can be modified or forged. However, facial images can be easily obtained with a couple of inexpensive fixed cameras. Good face recognition algorithms and appropriate preprocessing of the images can compensate for noise and slight variations in orientation, scale and illumination. Finally, technologies that require multiple individuals to use the same equipment to capture them biological characteristics potentially expose the to the transmission of germs and impurities from other s. However, face recognition is totally non-intrusive and does not carry any such health risks. (Jafri & Arabnia, 2009) Developing a fully automatic face recognition system is a challenging and complex topic in MATLAB due to various factors like pose and illumination variations, different age, gender, ethnicity, facial hair, occlusion, head motions, lower intensity of expressions and other PT1386619


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difficulties. Facial expressions are generated by contraction or relaxation of facial muscles or by other physiological processes such as coloring of the skin, tears in the eyes or sweat on the skin. Facial expression represents a particular pattern. In order to classify/recognize a pattern into appropriate class there is a need to extract information from the patterns and produce feature values. There are many face extraction methods reported by researchers in the field of face recognition. To combine image information and knowledge of the face is the main trend. In the 1970s, Goldstein, Harmon, and Lesk used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. In 1988, Kirby and Sirovich applied principle component analysis, a standard linear algebra technique, to the face recognition problem. This was considered somewhat of a milestone as it showed that less than one hundred values were required to accurately code a suitably aligned and normalized face image. In order to discriminate face candidates from the neck, ears and the incorrect segmented parts, shape analysis of the color segmentation result is necessary. One method involves first filling an ellipse to the segmented face for registration. Most of them do not take into consideration the misalignment caused by ear and neck. (Saber and Tekalp, 1998; Lee et al., 1996) Ian-Gang Wang, Eric sung in their article have proposed a morphological procedure to analyze the shape of segmented face region. This is an unexplored approach in face detection research. We have incorporated some of the methods proposed in this article. Several rules have been formulated for the task of locating the contour of the face. These rules were primarily based on the facial skeleton and knowledge of the face. The searching region of the face features reduces. The facial features such as mouth, nostrils and eyes can be more accurately located within the face contour. Terrillon et al., 1998 mentions the problem of how other body parts such as neck may lead to face localization error. A different approach was presented in where the features are first detected and then the contour was tracked using a deformable model. Brunelli and Poggio,1993 use dynamic programming to follow the outline in a gradient intensity map of elliptical projection of face image. Haalick and Shapiro, 1993 demonstrate how morphological operations can simplify the image data while preserving their essential shape characteristics and can eliminate irrelevances. Face feature detection is a multi-step process. (Sobottka and Pitas, 1998)

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Figure 2 Reduction in Error Rate for Face Recognition Algorithm (Ali Tarhini on software development and algorithms, 2017)

The above figure depicts the reduction in error rate for face recognition algorithms through the years. It can be seen that with the advancement of technology and as the years’ progress by the error rate has shown a considerable amount of reduction. In 1993, the error rate was 0.8 while in 2006 it has ceased to nearly zero. PROBLEM CONTEXT Face recognition is a specific and hard case of object recognition. The difficulty of this problem stems from the fact that in their most common form (i.e., the frontal view) faces appear to be roughly alike and the differences between them are quite subtle. Consequently, frontal face images form a very dense cluster in image space which makes it virtually impossible for traditional pattern recognition techniques to accurately discriminate among them with a high degree of success. Furthermore, the human face is not a unique, rigid object. Indeed, there are numerous factors that cause the appearance of the face to vary. The sources of variation in the facial appearance can be categorized into two groups: intrinsic factors and extrinsic ones. A. Intrinsic factors are due purely to the physical nature of the face and are independent of the observer. These factors can be further divided into two classes: intrapersonal and interpersonal. Intrapersonal factors are responsible for varying the facial appearance of the same person, some examples being age, facial expression and facial paraphernalia (facial hair, glasses, cosmetics, etc.). Interpersonal factors, however, are responsible for the differences in the facial appearance of different people, some examples being ethnicity and gender. B. Extrinsic factors cause the appearance of the face to alter via the interaction of light with the face and the observer. These factors include illumination, pose, scale and imaging parameters (e.g., resolution, focus, imaging, noise, etc.). (Jafri & Arabnia, 2009) PT1386619


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Although most current face recognition systems work well under constrained conditions (i.e., scenarios in which at least a few of the factors contributing to the variability between face images are controlled), the performance of most of these systems degrades rapidly when they are put to work under conditions where none of these factors are regulated. Some of the other problems in context of the project are mentioned below: 1. Given an image, to identify it as a face and/or extract face images from it. To retrieve the similar images (based on a heuristic) from the given database of face images. 2. Other problem is to identify certain criminals especially in identification technique used by the police. Face recognition helps to recognize the facial image in more efficient and accurate in 3. Order to match with the identity stored in the database. 4. In security system, many type of are used to access the private and confidential data. s and PINs are hard to and can be stolen or guessed. Face recognition is more 5. Secure because facial image had been used as the ID. It also helps to avoid any duplicated identification. OBJECTIVES 1. To recognize a sample face from a set of given faces. 2. Use of Principal Component Analysis [Using Eigenface approach]. 3. Use a simple approach for recognition and compare it with Eigenface approach. 4. Code the program in MATLAB 5. Implementation on the hardware 6. Testing the hardware LEARNING OBJECTIVES At the end of the research work and completion of project, the researcher should: 1. Be able understand the concept of face recognition 2. Be able understand the concept of RFID card 3. Be able to design the circuit 4. Be able to write the algorithm for the system 5. Be able to write the code 6. Be able to implement of the system on hardware 7. Be able to analyze the system

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PROJECT BENEFITS Face recognition is used for two primary tasks: 1. Verification (one-to-one matching): When presented with a face image of an unknown individual along with a claim of identity, ascertaining whether the individual is who he/she claims to be. 2. Identification (one-to-many matching): Given an image of an unknown individual, determining that person’s identity by comparing (possibly after encoding) that image with a database of (possibly encoded) images of known individuals. (Jafri & Arabnia, 2009) There are numerous application areas in which face recognition can be exploited for these two purposes, a few of which are outlined below. 1. Security (access control to buildings, airports/seaports, ATM machines and border checkpoints; computer/network security; email authentication on multimedia work stations). 2. Surveillance (a large number of CCTVs can be monitored to look for known criminals, drug offenders, etc. and authorities can be notified when one is located; for example, this procedure was used at the Super Bowl 2001 game at Tampa, Florida; in another instance, according to a CNN report, two cameras linked to state and national databases of sex offenders, missing children and alleged abductors have been installed recently at Royal Palm Middle School in Phoenix, Arizona). 3. General identity verification (electoral registration, banking, electronic commerce, identifying new born, national IDs, ports, drivers’ licenses, employee IDs). 4. Criminal justice systems (mug-shot/booking systems, post-event analysis, forensics). 5. Image database investigations (searching image databases of licensed drivers, benefit recipients, missing children, immigrants and police bookings). 6. “Smart Card” applications (in lieu of maintaining a database of facial images, the faceprint can be stored in a smart card, bar code or magnetic stripe, authentication of which is performed by matching the live image and the stored template). 7. Multi-media environments with adaptive human computer interfaces (part of ubiquitous or context aware systems, behavior monitoring at childcare or old people’s centers, recognizing a customer and assessing his needs). 8. Video indexing (labeling faces in video). 9. Witness face reconstruction. (Jafri & Arabnia, 2009) PT1386619


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In addition to these applications, the underlying techniques in the current face recognition technology have also been modified and used for related applications such as gender classification, expression recognition and facial feature recognition and tracking; each of these has its utility in various domains: for instance, expression recognition can be utilized in the field of medicine for intensive care monitoring while facial feature recognition and detection can be exploited for tracking a vehicle driver’s eyes and thus monitoring his fatigue, as well as for stress detection. Face recognition is also being used in conjunction with other biometrics such as speech, iris, fingerprint, ear and gait recognition in order to enhance the recognition performance of these methods. FUNCTIONALITIES The functionalities of the system are as followed: a. Recognition of face through MATLAB and authorizing the person which matches the image in database. b. Using RFID card for levelling up the level of the security. TECHNICAL SPECIFICATION MATLAB Software

2015 version

Arduino Software

Latest version

ATMEGA328 microcontroller

8 bit AVR

RFID reader

Works at radio frequency

RFID tags

Works at radio frequency

Type B Standard 2.0 USB cable

For burning code

Connectors

Wire

Common Voltage Hub

Provides range for voltage

Servo motor as door lock

FEASIBILTY STUDY Feasibility studies addresses things like where and how the business will operate. It provides in-depth details about the business to determine if and how it can succeed and serve as a valuable tool for developing a winning business plan. 1. Technical Feasibility 2. Economic Feasibility 3. Social Feasibility PT1386619


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4. Schedule Feasibility The feasibility of the system can be seen from the fact that one could many similar kinds of product available in the market. The table given below shows the various product and their website are given.

Commercial Product

Websites

SpotIt for face composite

http://spotit.itc.it/SpotIt.html

FaceSnap Recoder

http://www.facesnap.de/htdocs/english/index2.html

Biometric Systems, Inc.

http://www.biometrica.com

Visionsphere Technologies

http://www.visionspheretech.com/menu.htm

BioID sensor fusion

http://www.bioid.com

Eyematic Interfaces Inc.

http://www.eyematic.com

ImageWare Sofware

http://www.iwsinc.com

faces from ID-arts

http://www.id-arts.com

Keyware Technologies

http://www.keywareusa.com

Cognitec Systems

http://www.cognitec-systems.de

FaceKey Corp.

http://www.facekey.com

FaceVACS from Plettac

http://www.plettac-electronics.com

Viisage Technology

http://www.viisage.com

FaceIt from Visionics

http://www.FaceIt.com

TECHNICAL FEASIBILTY The project is considered technically feasible if the internal technical capability is sufficient to the project requirements. If the organization has the resources to develop or purchase, install and operate the system. Since, system technically requires hardware, software, network, and people involved in completing the Project. Thus, we need to ensure that, whether the required technology is available or not and whether the required resources for the system are available in of manpower, equipment’s, Software and Hardware. (This feasibility is further explained in the Technical Research.) Technically, the developer has to study and work upon various tools to develop the application. These tools are essential for the development and implementation of the proposed application. Kindly refer to section 7.2 of this documentation for a detailed report of tools used. Through proper research over technical requirements of the system the developer has concluded that this system is technically feasible PT1386619


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ECONOMIC FEASIBILTY The term "economic feasibility" is used to refer to the financial viability of a particular business project. This is usually a very important to carry out before starting any project since the main purpose of the system is profitable for the study of the organization. The project will benefit the organization in several ways the developer explained in the Justification section of the document. Economic feasibility is the cost and logistical perspective for a project or entrepreneurial effort. Before embarking on a new venture, most companies conduct an economic feasibility study, a study that analyzes the data to determine whether the cost of the new potential company ultimately pays off for the company. Economic feasibility is sometimes determined within an organization, while other times companies hire an external firm specializing in conducting economic feasibility studies for them.The purpose for assessing economic feasibility is mainly to identify the financial benefits and costs associated with the development project. Economic feasibility is referred as cost and benefit analysis for any system. The cost management process Includes: 1. Project cost estimation: It involves developing an approx. or estimate of the cost of the resource needed to complete the project. 2. Project cost budgeting: It involves allocating the overall cost estimate to individual work item to establish baseline for measuring performance. Main output are-cost baseline, project funding output of cost budget. 3. Project cost control: It involves controlling change to the project budget. Outputs are performance measurement, request change, corrective action and update of project management SOCIAL FEASIBILTY Operational feasibility of the measures to what extent the proposed system solves the problems identified in the project development stage; As the project intends to meet the requirements identified in the scope definition and how the system uses the opportunities identified in the scope definition. Before developing the system, the developer has carried out detailed research on different aspects. needs were collected through questionnaires and interview sessions. After analyzing the data collected for this method of primary and secondary research, the developer has created an original project of the system. All functions are dependent on the request highly. Therefore, there is no possibility that the system is rejected by s.

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SCHEDULE FEASIBILTY Schedule feasibility is defined as the possibility of a project being completed within its scheduled time frame. If the project has a high possibility of completion by the desired due date, then schedule feasibility is considered to be high. A project will fail if it takes too long to be completed before it is useful. It means guessing how long the system will take to develop. The main part of schedule feasibility report is Gantt Chart Developer use this chart for the time estimation. All the major and minor activities are shown in the Gantt chart with completion date for all major activities within the project that meets deadlines. (Refer to Appendix for Gantt chart). It consists of assess the work to which the time frame and the completion date for all major activities within the project that meets organizational deadlines and constraints for affecting change. The main parts of schedule feasibility report are: (Schwalbe, 2008, p. 215) Gantt Chart- The Developer has used this chart for the time estimation. Pert Chart- the developer has used this diagram to display critical tasks of the system DELIVERABLES At the end of this project, the deliverables would be: 1. Draft Proposal Form 2. Project Proposal Form 3. Ethical Form 4. Project progress related information and Log Sheet for supervisory meetings. 5. Hard copy documentation of the system. 6. Soft copy documentation of the system. 7. System application on CD. 8. Final Presentation and demonstration of the project. ETHICAL ISSUES No ethical issues are raised throughout the development of the complete project. Not any participant would be misled, not anyone’s contentious or sensitive issues will be involved, no harm to animal will be caused, participant’s ideas or views during the primary research will be kept confidential and each participation will be voluntary. For this assurance, the developer has also filled up a fast track ethical approval form before starting with the project. It ensures that the project will not be going to harm the society or anyone in anyways. It contains issues like no animal harm & not copying of data without prior permission of the author etc. and to ensure that the developer has filled up an ethical form and it will be provided in documentation. It also PT1386619


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ensures no information of will be leaked outside the system. CONCLUSION The Chapter 1 includes the background study for the system. Studying the system has inferred the purpose of the system and goals/ objectives of the system. After the purpose of the system is identified, the developer then came up with the benefits of the system to its intended end and how the system can benefit its s. The developer then cultivated the desirable features to be implemented in the proposed system to resolve the problems that were identified in the problem context section. The chapter has also included the deliverables the system will produce after it is completed. The limitations for the system have also been identified that limits the scope of the proposed project.

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CHAPTER 2 LITERATURE REVIEW Introduction A review of the literature search and evaluation of the available literature on a topic. It contains a description of all the literature that relates to a particular subject or topic, the main purpose of the review of the literature is to discuss the field and technical research project. It is like a secondary research for the project to be carried out using books, magazines, websites and other published sources. It is the study of the field, including the field work that develops the system to existing information on the market. It is a method of examining the existing elements, facilities and knowledge of a subject to be examined. It is a very effective technique that helps in gathering information on a topic. A review of the literature goes beyond the search for information and includes the identification and articulation of the relationships between literature and field research. Although the form of the literature review may vary according to different types of studies, the basic objectives remain constant. A review of the literature has four main objectives: 1. It surveys the literature in your chosen area of study. 2. It synthesizes the information in that literature into a summary. 3. It critically analyses the information gathered by identifying gaps in current knowledge; by showing limitations of theories and points of view; and by formulating areas for further research and reviewing areas of controversy. 4. It presents the literature in an organized way. Why write a literature review? The first step of any research project is to review the field. So, let’s think about surveying, synthesizing, critically analysing and presenting in more detail. A literature review does the following. 1. Identifies gaps in current knowledge. 2. It avoids reinventing the wheel – i.e. it saves wasting time researching something that’s already been done. 3. It allows one to show that the developer is building on a foundation of existing knowledge and ideas – i.e. carrying on from where others have already reached. 4. It identifies other people working in the same field. Knowing who has already worked in domain area and getting in touch with them can be an invaluable source of knowledge and . PT1386619


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5. It demonstrates the depth of one’s knowledge about his research. 6. It identifies the important works in the domain area and shows that the developer has read them. 7. It provides an intellectual context for the work, and enables to position the project in relation to others in the field. 8. It identifies opposing views. 9. It puts work done in perspective – are you doing something completely new, revisiting an old controversy in the light of new evidence, etc 10. It demonstrates research skills – i.e. one should not only know about work in his/her area but also know how to access it. 11. It identifies information and ideas that may be relevant to project. 12. It identifies methods that may be relevant to project. Justification of literature review The developer has decided to review the literature related to the current area of study because it will help the developer in knowing the background of the current project, what research progress has previously been made. The developer has conducted a research to study the similar systems to identify the problems or the controversies that will save the time of the developer by not repeating the mistakes already made in the current field of study. The developer would get an idea about how previous projects have been made, what technologies are suitable for such projects, do such project will conflict any points any future etc. Literature review assures the reader of the research about the significance of the research being done by the researcher and to gain interest of the reader. The developer/ researcher can utilize the results of the already made research to identify an auspicious direction for the future. He/she can learn from others and then integrate her own ideas into the existing ideas. So, the developer has decided to first review the work of others, learn from them and then move forward in the direction that will lead a successful project. Technical Literature Review A literature review is critical and in depth evaluation of previous research. Basically, it is a summary and synopsis of a particular area of research, allowing anybody reading the paper to establish why the researcher is pursuing this particular research program. A good literature review expands upon the reasons behind selecting a particular research question.

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Literature review is anything but not a chronological catalogue of all the sources, but an evaluation, integrating the previous research together, and also explaining how it integrates into the proposed research program. All the sides of an argument must be clearly explained, to avoid bias, and areas of agreement and disagreement should be highlighted. It is not a collection of quotes and paraphrasing from other sources. A good literature review should also have some evaluation of the quality and finding of the research. A good literature review should avoid the temptation of impressing the importance of a particular research program. The fact that a researcher is undertaking the research program speaks for its importance, and an educated reader may well be insulted that they are not allowed to judge the importance for themselves. They want to be re-assured that it is a serious paper, not a pseudoscientific sales ment. As a general rule, certainly for a longer review, each paragraph should address one point, and present and evaluate all of the evidence, from all of the differing points of view. The main purpose of the literature review is to identify the relevant research on the topic and demonstrate the knowledge of existing research to justify the need of proposed project. This review mainly shows, how wearable is working and how it makes the more attractive toward it and also how face recognition will be stabilized and ready to come in the work field. The researcher hypothesized that why don ‘t we use automated door lock system through face recognition if this much research is going on this technology and also want to add one more function in the system that is RFID card. Different survey already states the large number organization giving the order of making the all over the world. The main purpose of this literature review is to prove this hypothesis correct and define a clear scope to the project. Preliminary Research First study Research indicates that memory is reconstructive rather than reproductive, a distinction originally made in Bartlett's (1932) classic work. In other words, what people recognize or recall is seldom an exact replica of an original event, object, or face, but is rather a re-creation of what has been experienced. As people undertake the task of reconstructing the past, they are influenced by multiple variables including, but not limited to, the extent to which the target memory is associated with other memories (Roediger & McDermott, 1995), or is linked to multiple contexts. In some cases, people may be confident that an event, object, or face is familiar, but may be unable to identity in what particular context they have seen it before. This effect of familiarity (Schacter, 2001) has been targeted as a source of false recognition in eyewitness identifications. Memory is also influenced by attention (Stanny & PT1386619


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Johnson, 2000), encoding (the processes people use to bring information into long-term memory), the extent to which the encoding context resembles the retrieval context (Godden & Baddeley, 1975), and by the emotional content of the stimuli (LaBar & Phelps, 1998). With all these variables involved, it is not surprising that people have a tendency to make errors. Research papers used for literature review 1. Face Recognition Using PCA (Principal Component Analysis) Based Method by Tahia Fahrin Karim, Molla Shahadat Hossain Lipu, Md. Lushanur Rahman and Faria Sulatana Submitted in IEEE 2010. 2. Facial Expression Recognition System Using Neural Network and MATLAB by Nisha Thomas and Mercy Mathew Published in IEEE 2015. 3. A Practical Implementation of Face Detection by Using MATLAB Cascade Object Detector by Elena Alionte and Corneliu lazar Published in IEEE 2015 4. Hardware Architecture Design of Face Recognition System Based on FPGA (Field Programmable Gate Array) by Rakim Kumar Mondal, Md. Imran Khan, A.K. Mahbubul Hye and Asif Hussuian Published in IEEE 2015 5. Face Recognition Based On Auto-Switching Magnetic Door Lock System Using Microcontroller by Hamani Hassan Raudah Abu Bakar ahmad Thaqib Fawwaz Mokhtar Published by IEEE 2012 6. A Scalable ive RFID Based Multi- Indoor Location System by Shang Ma and Yuanchum Shi Published by IEEE 2011 7. Study of Automated Face Recognition System for Office Door Access Control Application by Ratnawati Ibrahim and Zalhan Mohd Zin Published by IEEE 2011 8. Application of Fuzzy Oscillation in the Field of Face Recognition by S. Bhattacharya, S.B.Roy and S. Saha Published by IEEE 2015 9. A Human Face Detection Method Based On Connected Component Analysis by S. Borah, S. Konwar, Dr. T. Tuithung and Rahul Rathi Published by IEEE 2014 All research paper discussed below one by one. Face Recognition Using PCA (Principal Component Analysis) Based Method by Tahia Fahrin Karim, Molla Shahadat Hossain Lipu, Md. Lushanur Rahman and Faria Sulatana Submitted in IEEE 2010 The research intended to develop face recognizing system using PCA based method. Programs were implemented on MATLAB. For matching the unknown images to known images

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techniques like Sum of Absolute difference (SAD), Sum of Squared Differences (SSD), Normalized Cross Correlation (NCC) etc. were used. The researcher referred different approaches like geometric approach, elastic face matching, neuron nets, video based, PCA approach and LDA based face recognition. The researcher favoured PCA above all the methods, in this the faces are represented in the form of Eigen faces components because it reduces the data dimensionally by performing a covariance analysis between factors. It explores correlations between samples or conditions. In PCA it was found that the system can reconstruct an efficient image by taking 20% of the information of the original image. Techniques like SAD, SSD and NCC were used to match the unknown images to the known images. The efficiency of the system can be increased by implementing a system based on cumulative characteristic of PCA and LDA. Facial Expression Recognition System Using Neural Network and MATLAB by Nisha Thomas and Mercy Mathew Published in IEEE 2015 The researcher here uses a feed forward method for identifying the facial expression of human being by a computer. The system was able to recognize four principal human expressionhappy, sad, and neutral and surprise. The performance of the system came out to be 0.0198 out of 0.0200. Artificial neural network is successful in solving pattern and classification and recognition problems. But the disadvantage of the system is its complexity and the cost of implementing it on the hardware would be very high. The neural network was able to solve problems related to gender classification, face recognition and classification of facial expression. A Practical Implementation of Face Detection by Using MATLAB Cascade Object Detector by Elena Alionte and Corneliu lazar Published in IEEE 2015 The researcher here implemented a practical face detector based on Viol-Jones algorithm using MATLAB Cascade detector. The researcher developed eight Eigen face detectors using the train cascade layer. Object detector function and tuning the number of cascade layer and the false rate alarm. The performance of the face detectors were analyzed for different tuning parameters. The efficiency of the system was tested by giving 100 positive and 100 negative inputs but due to high number of negative inputs some of the face detectors failed. The efficiency of the system can be increased by increasing the training period of the system.

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Hardware Architecture Design of Face Recognition System Based on FPGA (Field Programmable Gate Array) by Rakim Kumar Mondal, Md. Imran Khan, A.K. Mahbubul Hye and Asif Hussuian Published in IEEE 2015 The researcher suggests a hardware architecture for face recognition system. It discusses two types of approach one is image based and the other is video based. Further it can be classified into partially automated and fully automated. The methodology used in the paper are Principal Component Analysis (PCA) and Radial Basis Function (RBF). For developing the algorithm Fast Fourier Transform (FFT). The advantage of developing a FPGA is because of its ability to update to the functionalities or correct any error by re-programming the FPGA with system’s new version. The system is targeted for access control, face databases, face identification, human computer interaction, law enforcement, smart cards. Face Recognition Based On Auto-Switching Magnetic Door Lock System Using Microcontroller by Hamani Hassan Raudah Abu Bakar ahmad Thaqib Fawwaz Mokhtar Published by IEEE 2012 The researcher developed a GUI based face recognition system for door lock control using microcontroller. The method used to develop the algorithm is PCA but the comparison of the image in database and new image is done by face space which best defines as the variation of the known test image. Microcontrollers are used in this project because of its high speed performance, low cost and programming flexibility. USB serial communication is used to interface between GUI and PIC microcontroller that allows input data transmission from GUI to microcontroller. It was found that GUI can successfully switch on and off the magnetic lock when an authorized image from GUI database is identified. A Scalable ive RFID Based Multi- Indoor Location System by Shang Ma and Yuanchum Shi Published by IEEE 2011 The researcher here used RFID for indoor location system. The system proved to be accurate and cost effective. But the current implementations mainly use active tags which suffer the issues of batteries replacement, installation, maintenance and per unit cost. To address these challenges, the researcher proposed a ive RFID based system for localizing multi-s and detecting human motion from various types of embedded sensors to be supplemented. In addition a reliable transmission protocol to grantee the location data transition between RF nodes based on dynamic PRI. According to the performance analysis, the tracking accuracy is well assured. Its quick responsiveness and good scalability as accuracy as well as low cost on

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energy and infrastructure, make this system a more cost effective and easy to deploy solution for stable positioning. Study of Automated Face Recognition System for Office Door Access Control Application by Ratnawati Ibrahim and Zalhan Mohd Zin Published by IEEE 2011 The researcher here used feature based and image based algorithms. The former explores a set of geometric features, such as the distance between the eyes or the size of the eyes, and uses these measures to represent the given face. These methods are somewhat invariant to changes in illumination and can partially compensates for changes in camera location. However, they are sensitive to aging and facial expressions. In this paper, an automated face recognition system application was designed for the purpose of door access control application. Here the face recognition system developed is based on the well-known Eigen face technique which is derived from Principal Component Analysis (PCA). Application of Fuzzy Oscillation in the Field of Face Recognition by S. Bhattacharya, S.B.Roy and S. Saha Published by IEEE 2015 The aim of the paper was to propose a new algorithm for face recognition and its functionality is shown while it is used to recognize an unknown image from a set of known images. The theorem proposed for fuzzy logic mx oscillation was mathematically verified with a single face image with few pixel intensity. The advantage of using this algorithm is that it produces better accuracy with less computation cost. A Human Face Detection Method Based On Connected Component Analysis by S. Borah, S. Konwar, Dr. T. Tuithung and Rahul Rathi Published by IEEE 2014 The paper suggests that in an automatic face recognition system the first step is to segment the face in an image or video irrespective of whether the background is simple or clustered. The proposed algorithm can be used for facial point localization and its implementation on human facial expression detection. Conclusion of Literature Review The literature review is the bank bone of a project without no system can be as it forms the background of the whole i.e. how the very first time research was done on the project which is included in the preliminary study. The purpose of literature review is to give a brief knowledge about the technical aspects of the project, it helps in deciding the method which the researcher should follow for the purpose of developing the system. In the above literature review many methods like geometric feature, neural network, principal component analysis (PCA), LDA, feed forward method were discussed. Each has its own advantages and disadvantage. Out of PT1386619


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the above methods the researcher chose PCA as it gave up to 80% of efficiency and retrieve the image with only 20% of the total data available. But this system needs proper training before it goes out for training and its uses Eigen face values and its algorithm as its method for coding. The researcher has also incorporated the same method with a slightly different algorithm in his system i.e. to be developed. Another that the researcher has in corporate in his project is of RFID card which is also a widely-used technology in now a day. The researcher aims at developing an automated door lock system using face recognition as its key component and the card as an additional feature to increase the security level.

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CHAPTER 3 PROJECT MANAGEMENT Introduction Project Management as "the application of knowledge, skills, tools and techniques to a broad range of activities in order to meet the requirements of a particular project." It is essentially aimed at producing an end product that will effect some change for the benefit of the organization that instigated the project. It is the initiation, planning and control of a range of tasks required to deliver this end product. Projects that require formal management are those that: 1. Produce something new or altered, tangible or intangible; 2. Have a finite time span: a definite start and end; 3. Are likely to be complex in of work or groups involved; 4. Require the management of change. 5. Require the management of risks.

Project Management A project is temporary which means it has a defined beginning and end in time, and therefore defined scope and resources. And a project is unique which means it is not a routine operation, but a specific set of operations designed to accomplish a singular goal. So, a project team often includes people who don’t usually work together – sometimes from different organizations and across multiple geographies.

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The development of software for an improved business process, the construction of a building or bridge, the relief effort after a natural disaster, the expansion of sales into a new geographic market — all are projects. And all must be expertly managed to deliver the on-time, on-budget results, learning and integration that organizations need. Project management, then, is the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements. It has always been practiced informally, but began to emerge as a distinct profession in the mid-20th century. Project management processes fall into five groups: 1. Initiating: - An idea for a project will be carefully examined to determine whether or not it benefits the organization. During this phase, a decision-making team will identify if the project can realistically be completed. 2. Planning: - A project plan, project charter and/or project scope may be put in writing, outlining the work to be performed. During this phase, a team should prioritize the project, calculate a budget and schedule, and determine what resources are needed. 3. Executing: - Resources tasks are distributed and teams are informed of responsibilities. This is a good time to bring up important project related information. 4. Monitoring and Controlling: - Project managers will compare project status and progress to the actual plan, as resources perform the scheduled work. During this phase, project managers may need to adjust schedules or do what is necessary to keep the project on track. 5. Closing: - After project tasks are completed and the client has approved the outcome, an evaluation is necessary to highlight project success and/or learn from project history. Projects and project management processes vary from industry to industry; however, these are more traditional elements of a project. The overarching goal is typically to offer a product, change a process or to solve a problem in order to benefit the organization. (institute, 2017) Project management knowledge draws on ten areas: 1. Integration: - It’s about bringing together everything one knows, so that managing the project holistically and not in individual process chunks. Because of that, it’s easier to study this knowledge area last.

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2. Scope: - ‘Scope’ is the way to define what project will deliver. Scope management is all about making sure that everyone is clear about what the project is for and what it includes. It covers collecting requirements and preparing the work breakdown structure. 3. Time: - Project time management isn’t about being personally more effective. It relates to managing the time people are spending on their project tasks, and how long the project takes overall. This knowledge area helps to understand the activities in the project, the sequence of those activities, and how long they are going to take. 4. Cost: - The big activity in this knowledge area is preparing budget which includes working out how much each task is going to cost and then determining project’s overall budget forecast. 5. Quality: - Project quality management is quite a small knowledge area, as it only covers three processes. This area is where we learn about and set up the quality control and quality management activities on the project so that you can be confident the result will meet your customers’ expectations. 6. Procurement: - Procurement management isn’t something that we’ll have to do on all projects, but it is common. This knowledge area s all procurement and supplier work from planning what we need to buy, to going through the tendering and purchasing process to managing the work of the supplier and closing the contract when the project is finished. 7. Human resources: - Project human resource management relates to how to run project team. First, understand what resources are needed to be able to complete the project, then put team together. 8. Communications: - Given that a project manager’s job is often said to be about 80% communication, this is another small knowledge area. The three processes are planning, managing and controlling project communications. 9. Risk management: - The first step in project risk management is planning the risk

management work, and then we quickly move on to identifying risks and understanding how to assess risks on the project. There is a lot of detail in this knowledge area, specifically around how we perform quantitative and qualitative risk assessments. Risk management isn’t a one-off activity, though, and this knowledge area also covers controlling the project risks going forward through the project life cycle.

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10. Stakeholder management: - The final knowledge area is, I think, the most important. This takes through the journey of identifying stakeholders, understanding their role and needs in the project and ensuring that we can deliver those. (Dybala, 2017) All management is concerned with these, of course. But project management brings a unique focus shaped by the goals, resources and schedule of each project. The value of that focus is proved by the rapid, worldwide growth of project management: 

as a recognized and strategic organizational competence



as a subject for training and education



as a career path

In order to execute a project successfully, the developer should be ed by a set of tools. These tools can be specifically designed tools or regular productivity tools that can be adopted for project management work. The use of such tools usually makes the project managers work easy as well as it standardizes the work and the routine of a project manager. For completion of project on due time proper planning is required so as a researcher the idea must be there for complexity of project, difficulties of project and duration which is taken by each step must be noted. So, project management tool is requiring to work according to the plan which can be done by various methods. A number of charting methods, such as the Gantt chart and PERT chart have been developed as tools to create a graphic representation of a project plan and its current status. Project Planning The design of the project involves the gathering of relevant information through country feasibility studies and market research involve; at large and carrying out preliminary. The output of the planning process will be the project cost benefit analysis to find out if this project will be sufficiently beneficial to target group, owners or proposal. Project analysis and planning are normally undertaken during the design and preparation stage and involve: 1. Analysis of Market Proposals. 2. Analyses the demand for the projects output. 3. Analysis of technical feasibility. 4. Analyses technical feasibility in of inputs, technique, scale, engineering, machinery, treatment of waste and others. 5. Analysis of financial feasibility. 6. Analyses the financial viability by looking at projected outlay and receipts among others. PT1386619


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7. Analysis of Economic feasibility. 8. Analyses the benefits of the project to the nation, National resource, allocation and distributional effects. 9. Analysis of Ecological effects - Analyses the environment through environmental impact assessment.

Figure 3 Block diagram for project management

Planning Process As the project follows the methodology mentioned above, the phases included in the methodology are further broken into a project plan and each activity is assigned time according to the critical tasks involved in it. The whole project will be completed in several prototypes based on different functionalities and requirements. First set of prototypes will cover core functionalities of the system while second set of prototypes will cover enhanced and special functionalities. Further prototypes can also be presented depending upon needs. PT1386619


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Development Plan Total duration-32 weeks Phases and timeline: Sr. No. 1.

2.

3. 4.

5.

6.

7. 8. 9. 10.

11. 12. 13. 14.

TOPICS Project Definition  Idea Generation  Project Title Selection  Draft Proposal Form Project Planning  Project Proposal Form  Scheduling Resources Plan Literature Review Proposed Methodology  Block Diagram  Component Used  Circuit Diagram  Software Used Data Gathering Technique Used  Questionnaires  Interview Project Management  Pert Chart  Gantt Chart Costing and Health Risk Midpoint Critical Appraisal and Submission System Implementation Testing and Results  Hardware Testing  Result  Discussion Conclusion Recommendation Referencing and Bibliography Final Submission

DURATION (32 WEEKS) 2 Weeks

2 Weeks 1 Week

1 Week

2 Weeks

1 Week 1 Week 1 Week 12 Weeks 3 Weeks

1 Week 1 Week 1 Week 1 Week

Time Management Time management is a most important step for the success of any project on time. Firstly, the project is broken down into a number of tasks that have to be performed. This process should be completed before the implementation phase of the project. For the proposed project, it is necessary to create a time plan by setting the objectives and then subdividing them into manageable sequence with the deadlines attached to it. The developer must identify the specific scheduled activities to be performed and time period needed to complete these activities. Then an action plan should be made for all the tasks needed PT1386619


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to be carried out to achieve the objective. The developer must be aware of all the tasks to done to make the project a success. The amount of time each task will take to complete should also be estimated clearly. The next step is to decide which work is most important and set the priorities to the tasks. For time management analysis, Gantt Charts and PERT Chart are useful tools any project. The developer is using two-time management tools to manage the proposed project which are Gantt chart and PERT chart. Gantt Chart A Gantt chart, commonly used in project management, is one of the most popular and useful ways of showing activities (tasks or events) displayed against time. On the left of the chart is a list of the activities and along the top is a suitable time scale. Each activity is represented by a bar; the position and length of the bar reflects the start date, duration and end date of the activity. (Gupta, 2017) As the entire life of the project is represented in a visual format, it becomes easy to decipher critical information related to the project such as its scope and keep a bird’s eye view on everything. Gantt charts are useful for planning and scheduling projects. They help to assess how long a project should take, determine the resources needed, and plan the order in which the task will be completed. They're also helpful for managing the dependencies between tasks. While there are a number of reasons to use Gantt charts below are five prime reasons they are often advantageous. 1. Avoid Completion Confusion: Gantt charts were created to keep s on track, providing a visual timeline for starting and finishing specific tasks. By providing a visual overview of milestones and other key dates, these charts are thought to offer a more understandable and memorable method of maintaining timescale-based tasks and deliverables whether tracked on a daily, weekly, monthly or yearly basis. 2. Keep Everyone on the Same Page: Where there is a visual framework for the work to be done, there are fewer chances for misunderstanding, especially when it comes to highly complex tasks. Using Gantt charts allow all types of stakeholders to have the same information, set mutually understood expectations, and conduct their efforts according to the desired protocol. 3. Understand Task Relationships: These charts can make clear how various tasks are interrelated and perhaps rely on the completion of another to meet specific objectives. PT1386619


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These task relationships revolve around understanding the timing of each task, which then impacts other tasks listed. This can better assure the optimum work flow, maximized productivity and overall project success. 4. Effectively Allocate Resources: By being able to look ahead on the Gantt chart, s can clearly discern where resources need to be anticipated, allocated or shared to maximize the use of those resources. The more closely the chart is followed, the better chance there is of keeping project costs within budget while also better assuring ontime completion. 5. Get a Handle on the Future: While it is often easy to get caught up in day-to-day tasks as detailed on a chart, Gantt chart advantages include helping decision-makers look farther ahead to ensure each given project is working toward the achievement the organization’s long-term strategic objectives. (Gupta, 2017) For Gantt chart refer to appendix PERT chart PERT, or project/program evaluation review technique, s for inevitable changes that occur in a project’s schedule that impact the project finish. The technique was initially developed by the Navy in the 1950s for the Polaris submarine project. Today, PERT charts are used by teams across industries looking to plan and execute their projects more effectively. A true PERT chart involves a 3-point estimation technique, which assigns three possible duration estimates to each task: 1. Optimistic 2. Pessimistic 3. Most likely Estimating duration this way allows managers to calculate a more realistic project schedule. “PERT charts are an excellent training tool for a project controls person to truly understand the underpinnings of a schedule,” says PMFocus’s Patterson. For this project, the developer has used activity on arrow pert chart to evaluate the time management of the project. The Earliest Completion Time (ECT) i.e. the minimum amount of time needed to complete all the activities that precedes every event is mention in the upper part of the circle and the Latest Completion Time (LCT) which is the latest time needed at which the event can occur without delaying the overall project is mention in the lower part of the circle. PT1386619


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Figure 4 Depiction of a single block in PERT chart

The critical path is the path of the tasks which cannot be delayed and project will not move forward without completing these tasks. In the pert chart drawn below, black lined tasks which are from task 1, 4, 5, 7, 8, 9, 10, 11 and 12 indicates the critical path and these tasks cannot be delayed. Task 4 is dependent upon task 2 and 3 and task 7 depends upon 6. It means 7 cannot be starting before task 6 is not completed Activity

Predecessor

Name of Activity

A

-

Searching of FYP topics

B

A

Approval of final topic

C

B

Background Research

D

C

Research Methodology

E

C

Project Proposal

F

E

Midterm Submission

G

F

System development

H

G

Testing

I

H

Result and Conclusion

J

I

Final documentation

Refer to Appendix Project risk management issues Risk Management is the process of identifying, analyzing and responding to risk factors throughout the life of a project and in the best interests of its objectives. Proper risk management implies control of possible future events and is proactive rather than reactive. Proper risk management will reduce not only the likelihood of an event occurring, but also the magnitude of its impact. Risk management should therefore be done early on in the life cycle of the project as well as on an on-going basis.

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Risk Management Systems are designed to just identify the risk. The system must also quantify the risk and predict the impact on the project. No proper project planning: If the project is not planned properly, it will lead to a risk of not meeting the deadlines and the project can go out of budget. So, proper project planning is needed for the project to be a success. Various Components available on simulation software: Every project is firstly simulated and then tested on the hardware. But in some cases, the components to be used in the circuit are not available on the software. So, the risk simulation delay can be there in the project. And if the project is directly designed on the hardware, there are chances that the circuit might not work. Components unavailability in the market: Sometimes the components become unavailable in the market as these components are it used so frequently. This situation may be very risky as it can lead to the delay in the project implementation and it is very clear that hardware implementation takes maximum time out of all the milestones of the project. Components not working: The proposed system is making use of electronics components and sometimes these components may not work properly if they are not properly calibrated which may prove risky. So, components should be selected with proper calibration and they should be first tested and then applied in the system. Conclusion Project management has been designed to finish the project on due time. For this project, the tool has used as gain chart and pert chart. 263 days is required to complete the project as per plan and this duration is known as earliest start time. Minimum 263 days will be required to complete the whole deg and study of real time face recognition system using MATLAB with RFID for door lock system. Some of the activity or work can be done parallel with other activity according to the pert chart management. This will help developer to watch and regulate the work on time and remind the developer to finish the activity task on or before deadline of the task.

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CHAPTER 4 RESEARCH METHODOLOGY Introduction The project is being developed by a developer as a student so it is better to adopt a technology that may provide flexibility in the development. The general sequence to evaluate a project includes planning, analysis, design, implementation and testing. As the proposed system is a research based one, so the suitable methodology for this proposed system is spiral method. There are often occasions where the requirements are not well formed or understood by the s, where it is difficult to specify the requirements. So, in such situation this it is necessary that developer knows what actually the customer wants and try to fulfill the requirements of the customer. By using this methodology mistakes in the requirements can be corrected and gets . It allows customer and developer to determine and to react to risks at each evolutionary level. It is a lifecycle where the design, develop, test phases are repeated several times before the end product is complete. It uses prototyping as a risk reduction mechanism. The spiral model demands a direct consideration of technical risks at all stages of the project, and should reduce risk before become problematic. It has the advance approach on setting project objectives, project risk management and project planning into the overall development cycle. Project Stages – this will have five stages and these are: 1. Research and Planning 2. Analysis. 3. Design. 4. Development. 5. Implementation. 6. During this phase the research of the whole project have to be done which will include different technology, language, platform, requirement etc. which is required during the development of the methodology of the proposed project i.e. includes various phases starting from over viewing the block diagram of the project, selection of hardware and software to be used, all the testing points whether hardware or software which are to be used in the project and how the system will be implemented and its performance will be tested. After this the primary and the secondary researches were undertaken by the developer. All the information and the requirements of the project were obtained using various data gathering

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techniques like questionnaire, interview, observation, case study, etc. so that the needs can be finalized before deg and implementation. The data collected from the research were analyzed and conclusions were made on the basis of research. After analyzing the results of primary research, developer concentrates on the secondary research of the project that includes the selection of methodology to be used for completing the project and technical research about the project which would include the hardware and software technology to be used in the project. By completing the primary research phase of the project the requirements are clear in developer’s mind. So, keeping these parameters in mind, secondary research is done to review the technical aspects of the project which would help to implement the project successfully. After doing the primary and the secondary research analysis deign of the project was prepared and the solid design was also prepared and the specification of the software and hardware used in the project were also justified. This chapter gives the outlook of the methodology and software and hardware requirements for the project were also justified. Research Methodology This chapter will cover the methodology that is proposed to complete the project and make it working successfully. The success of any project depends upon the type of methodology or the pathway adopted by the developer to complete the project. Methodology is the proper development plan that gives a sequence of steps to be followed to finish the project on time and within budget. If the methodology of the project is not set, the quality and performance of the project lags behind and project becomes difficult to manage which ultimately lead to the failure of the project. The project is being developed by the developer as a student so it is better to adopt a technology that may provide flexibility in the development. The general sequence to evaluate a project includes planning, analysis, design, implementation and testing. The methodology of the proposed project i.e. Real Time Face Recognition System using MATLAB with RFID for Door Lock System includes various phases starting from over viewing the block diagram of the project, selection of hardware and software to be used, all the testing points whether hardware or software which are to be used in the project and how the system will be implemented and its performance will be tested.

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Planning Planning is an effort to produce fundamental decisions which is followed by a sequence of actions. It includes various methodologies and frameworks for the strategic planning and management. Basic phases which are included in this framework are: 1. Identifying goals and objectives to be achieved. 2. Analyzing and assessing the current situation of the system. 3. Formulating strategy to overcome the flaws in the current system. 4. Execution of the strategy. 5. Evaluation of performance or also termed as management phase. Research is the first key factor of planning for developing any system. Planning includes primary and secondary research which was conducted by the researcher during the initial phase of developing the system. Planning has two stages that are data collection and finalizing the hardware and software requirements of the project based on the analysis of the data collected. Data Collection using Research Methods Data collection is an important stage for the development of any project. Research is done by the researcher using various methods to finalize the needs and requirements of the from the project being developed. All the information and the requirements of the project are obtained using various data gathering techniques like questionnaire, interview, observation, case study, etc. so that the needs can be finalized before deg and implementation. The data collected from the research will be analyzed and conclusions are made on the basis of research. There are two types of researches namely, primary and secondary research. Research method:

Figure 5 Types of research method

Primary Research Primary research is conducted by the developer during the initial stage of making the project to gather the information from the participants to be involved in the project so that the project

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being developed can be directly acceptable by the industry and also by the end s of the project. Interview is also a common and important data gathering technique which is used to collect the information regarding the needs of the system. It is a formal meeting with some expert which may be face to face, telephonic. It is good method for exploring the information in detail and encouraging people to respond. But this is a time-consuming method and sometimes is may become difficult to visit people for conducting interview. Interview is a more personal means of research than the questionnaires and this is completed but the interviewer on the basis of the sayings of the respondent. For the proposed, the developer has used face to face interviews for technical data gathering that enables the developer to gather desired information rapidly and record the interview of expert person with proper evidence. Data gathering techniques Face to face interviews are the best technique to collect information from a system or a company customer. Using this method, the developer can know about the needs of the along with all small specifications. The following are tetchiness to gathering information: 1. Observation 2. Interview 3. Questionnaire and survey 4. Focus group 5. Case studies Observation Direct observation of work performance is an excellent means of gathering data. Observations are usually done in conjunction with another data gathering method that is used to fill in the gaps and answer questions. Interview Interviews are conducted one-on-one or with a small group (the smaller the better so that everyone has a chance to contribute fully). Interviews can be used throughout the data gathering process, but they are perhaps most useful during the performance analysis stage, when you are trying to determine what the real performance deficiency is. Although the process is timeconsuming, it is useful because you can gather specific information and ask follow-up questions to get more detail on items of particular interest.

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Questionnaire and survey This is a part of interviews and is a very effective technique of gathering information regarding anything specific. Also in context of this technique will be a great help as it will help the developer to collect requirements directly from the s i.e. those who will use the e-database & table’s database & tables, like students, workers and the people who will use e-database & tables for communication. The developer can go for face to face questions with these people. This will help the developer to understand the problems in present system, if any and also give an idea of a ’s or customer’s mindset. Surveys are used when you want to gather data from a large number of people and when it is impractical to meet them all face to face. Surveys can be both formal (where the results are subject to statistical reliability and validity) and informal (where results are anecdotal). In the developing-world reproductive health context, there are relatively few commercially designed instruments. Although PRIME might be able to design some generic survey instruments, the unique problems and country differences might limit their usefulness. Clearly, it is expensive to design a survey for one performance improvement effort, so while we include surveys in this table, we are not optimistic about their applicability (except on a small, informal basis). Focus Group Focus groups provide insights into how people think and provide a deeper understanding of the phenomena being studied. While a valuable research tool, surveys generally ask closed-ended questions that may limit the that can be gained from a respondent. A method to gain more in-depth information to supplement surveys is interviews; conducting interviews, however, can be an expensive proposition that can exceed the available resources. Focus groups are group interviews that give the developer the ability to capture deeper information more economically than individual interviews. Economy is an important benefit but there are other benefits of focus groups when compared to interviews. Group interaction and non-verbal communication are primary benefits of focus groups. Group interaction between of the target population during focus groups may encourage participants to make connections to various concepts through the discussions that may not occur during individual interviews. A skilled facilitator can encourage these group interactions to capture this data to provide a more comprehensive understanding of what is being studied. Non-verbal communication is also data that can be captured in focus groups. Participants within a focus group may respond very differently to a topic.

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Case studies A case study is a report about a person, group, or situation that has been studied. If the case study, for instance, is about a group, it describes the behavior of the group as a whole, not the behavior of each individual in the group. Documented study of a specific real-life situation or imagined scenario, used as a training tool in business schools and firms. Students or trainees are required to analyze the prescribed cases and present their interpretations or solutions, ed by the line of reasoning employed and assumptions made. Conclusion of Questionnaire In the survey total 33 participants participated out of which 80% were male rest female. The response filled in the questionnaire were sent online and then the analysis was done. The survey was conducted to know about what reaction would the public give when such a system is introduced, the analysis was very ive in context of the project as all of them wanted to install the system at their places. The reason behind such reaction was mostly due to increase the security level as in today’s world no can be trusted. Further in the survey it was found that most of them were willing to pay 5000 to 10000 rupees for the installation of the system. When they were asked about where would they like to install such system the answer did not vary much because the system has its application in many field thus the target audience of the system is also quite strong. From technical view point people liked to keep the record of the system being used. For the question of problem that could be encountered got mixed responses for late response, error in face detection, problems in hardware implementation. Conclusion of Interview Question Total number of interview were 7, were asked the same question they belonged to various fields. This helped developer to analysis the needs to variety of the people. These interviewees involved a banker, servicemen, IT professional, HR manager etc. Overall one can conclude that the proposed system had got good response and most of them were in the favor of installing the system at their homes, offices, banks other places. Some of future enhancement that were suggested are like adding alarm when some intruder tries to the system. Sending notification to the authorized person whenever someone goes by the system. One more possibility is to send a message to police when some intruder tries to by the system more than 3 or 5 times. Conclusion of Focus Group Question Automated door lock system is innovative technology which has come into lights in the past decade. The can be automated through many ways like fingerprint, RFID, and many PT1386619


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others. Here in this project the door is automated through Face recognition as well as RFID. These two feature upholds the level of security many time as one entering through must have proper authorization as well as a card with itself to enter. Thus, the security of the system is increased many folds. The problems that one could encounter while installing the system are hardware installing, error in detecting faces, late response time, not training the system properly, proper working of smartcard etc. The efficiency of the system is generally good but can be increased by using proper algorithm and good quality components. The general cost of installing should not be high as it should be in the range of 5000 to 10000 because the system is also intended to be used by general public. So, if the cost is increased then that would limit the target audience so keeping in mind all the factors the range if 5000 to 10000 is perfect. For the further enhancement of the system in of feature can be done by adding image processing tools, and some features like alarm, notification, one time , mail to the authorized person each time a person enters the system. Conclusion

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CHAPTER 5 TECHNICAL RESEARCH Introduction Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identification and the verification of identity claims. It brings together the promise of other biometric systems, which attempt to tie identity to individually distinctive features of the body, and the more familiar functionality of visual surveillance systems. This report develops a technical analysis that bridges the technical and social-scientific literatures on FRT and addresses the unique challenges and concerns that attend its development, evaluation, and specific operational uses, contexts, and goals. It highlights the potential and limitations of the technology, noting those tasks for which it seems ready for deployment, those areas where performance obstacles may be overcome by future technological developments or sound operating procedures, and still other issues which appear intractable. Its concern with efficacy extends to ethical considerations. (Introna & Nissenbaum, 2017) The main findings and recommendations of the report are broken down into three broad categories: performance, evaluation and operation. These findings and recommendations employ certain technical concepts and language that are explained and explored in the further in this chapter. Performance 1. FRT has proven effective, with relatively small populations in controlled environments, for the verification of identity claims, in which an image of an individual’s face is matched to a pre-existing image “on-file” associated with the claimed identity (the verification task). It performs rather poorly in more complex attempts to identify individuals who do not voluntarily self-identify, in which the FRT seeks to match an individual’s face with any possible image “on-file” (the identification task). Specifically, the “face in the crowd” scenario, in which a face is picked out from a crowd in an uncontrolled environment, is unlikely to become an operational reality for the foreseeable future. (Introna & Nissenbaum, 2017) 2. FRT can only recognize a face if a specific individual’s face has already been added to (enrolled in) the system in advance. The conditions of enrollment—voluntary or otherwise—and the quality of the resulting image (the gallery image) have significant impact on the final efficiency of FRT. Image quality is more significant than any other single factor in the overall performance of FRT. (Introna & Nissenbaum, 2017)

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3. If certain existing standards for images (ANSI INCITS 385-2004 and ISO/IEC 197945:2005) are met or exceeded, most of the current, top-performing FRT could well deliver a high level of accuracy for the verification task. Given that images at the site of verification or identification (the probe image) are often captured on low quality video, meeting these standards is no small feat, and has yet to be achieved in practice. (Introna & Nissenbaum, 2017) 4. Performance is also contingent on a number of other known factors, the most significant of which are: 

Environment The more similar the environments of the images to be compared (background, lighting conditions, camera distance, and thus the size and orientation of the head), the better the FRT will perform. (Introna & Nissenbaum, 2017)



Image age The less time that has elapsed between the images to be compared, the better the FRT will perform. (Introna & Nissenbaum, 2017)



Consistent camera use The more similar the optical characteristics of the camera used for the enrollment process and for obtaining the on-site image (light intensity, focal length, color balance, etc.), the better the FRT will perform. (Introna & Nissenbaum, 2017)



Gallery size Given that the number of possible images that enter the gallery as near-identical mathematical representations (biometric doubles) increases as the size of the gallery increases, restricting the size of the gallery in “open set” identification applications (such as watch list applications) may help maintain the integrity of the system and increase overall performance. (Introna & Nissenbaum, 2017)

5. The selection and composition of images that are used to develop FRT algorithms are crucial in shaping the eventual performance of the system. (Introna & Nissenbaum, 2017) Evaluation a. Many of the existing evaluation results do not lend themselves to clear comparisons or definitive conclusions. The results of “close set” performance evaluations, for instance, which are based on the assumption that all possible individuals who might be encountered by the FRT are known in advance (i.e., there are no outside imposters), PT1386619


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cannot be compared across different tests or with “open set” (i.e., where there could be imposters) performance figures, and do not reflect or predict performance of an FRT in operational conditions (which are always “open set”). “Close set” evaluation results are contingent on the size of the gallery and rank number in the specific evaluation; they are thus fundamentally incommensurate with one another. “Open set” evaluation results are equally difficult to compare, as there is no way to predict in advance the number of imposters an FRT might encounter and therefore produce a standard performance baseline. (Introna & Nissenbaum, 2017) b. The current lack of publicly available data on operational as compared to laboratory— evaluations of FRT is a major concern for organizations that may want to consider the use of FRT. Without such evaluations, organizations are dependent on claims made by the FRT vendors themselves. (Introna & Nissenbaum, 2017) c. Evaluations should always include tests under full operational conditions, as these are the only tests that offer a real-world measure of the practical capabilities of FRT. These results, however, should not be casually generalized to other operational conditions. (Introna & Nissenbaum, 2017) d. More informative and rigorous tests would make use of gallery and evaluation images compiled by an independent third party, under a variety of conditions with a variety of cameras. (Introna & Nissenbaum, 2017) e. Evaluation results must be read with careful attention to pre-existing correlations between the images used to develop and train the FRT algorithm and the images that are then used to evaluate the FRT algorithm and system. Tightly correlated training (or gallery) and evaluation data could artificially inflate the results of performance evaluations. (Introna & Nissenbaum, 2017) Operation a. It is up to a system’s developers and operators to determine at what threshold of similarity between a probe and gallery image (the similarity score threshold) they wish the system to recognize an individual. Threshold decisions will always be a matter of policy and should be context and use-specific. (Introna & Nissenbaum, 2017) b. For instance, a system with a high threshold, which demands a high similarity score to establish credible recognition in the verification task, would decrease the number of individuals who slip past the system (false accept mistakes), but would also increase the number of individuals who would be incorrectly rejected (false reject mistakes). PT1386619


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These trade-offs must be determined, with a clear sense of how to deal with the inevitable false rejections and acceptances. (Introna & Nissenbaum, 2017) c. The rank number, which is the number of rank-ordered candidates on a list of the percent most likely matches for any given probe image, is a matter of policy determination. At rank 10, for example, successful recognition would be said to have occurred if the specific individual appeared as any of the top 10 candidates. (Introna & Nissenbaum, 2017) d. The images that are used to develop and train the FRT algorithm and system should reflect, as much as possible, the operational conditions under which the system will perform, both in of the characteristics of the individuals in the images (ethnicity, race, gender, age, etc.) and the conditions under which the images are captured (illumination, pose, the orientation of the face, etc.). This will facilitate a high level of performance. (Introna & Nissenbaum, 2017)

Face Recognition System (FRS) Tasks FRS can typically be used for three different tasks, or combinations of tasks: verification, identification, and watch list. Each of these represents distinctive challenges to the implementation and use of FRT as well as other biometric technologies. (Introna & Nissenbaum, 2017) Verification Verification or authentication is the simplest task for a FRS. An individual with a pre-existing relationship with an institution (and therefore already enrolled in the reference database or gallery) presents his or her biometric characteristics (face or probe image) to the system, claiming to be in the reference database or gallery (i.e. claiming to be a legitimate identity). The system must then attempt to match the probe image with the particular, claimed template in the reference database. This is a one-to-one matching task since the system does not need to check every record in the database but only that which corresponds to the claimed identity PT1386619


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(using some form of identifier such as an employee number to access the record in the reference database). There are two possible outcomes: the person is not recognized or the person is recognized. If the person is not recognized (i.e., the identity is not verified) it might be because the person is an imposter (i.e., is making an illegitimate identity claim) or because the system made a mistake (this mistake is referred to as a false reject). The system may also make a mistake in accepting a claim when it is in fact false (this is referred to as a false accept). The relationship between these different outcomes in the verification task is indicated in Figure given below. (Introna & Nissenbaum, 2017)

Figure 6 Possible outcomes in verification task (Introna & Nissenbaum, 2017)

Identification Identification is a more complex task than verification. In this case, the FRS is provided a probe image to attempt to match it with a biometric reference in the gallery (or not). This represents a one-to-many problem. In addition, we need to further differentiate between closed-set identification problems and open-set identification problems. In a closed-set identification problem we want to identify a person that we know is in the reference database or gallery (in other words for any possible identification we want to make we know beforehand that the person to be identified is in the database). Open-set identification is more complex in that we do not know in advance whether the person to be identified is or is not in the reference database. The outcome of these two identification problems will be interpreted differently. If there is no match in the closed-set identification then we know the system has made a mistake (i.e., identification has failed (a false negative)). However, in the open-set problem we do not know whether the system made a mistake or whether the identity is simply not in the reference database in the first instance. Real-world identification applications tend to be open-set identification problems rather than closed-set identification problems. (Introna & Nissenbaum, 2017) PT1386619


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Figure 7 Possible outcomes in Identification task (Introna & Nissenbaum, 2017)

Watch List The watch list task is a specific case of an open-set identification task. In the watch list task, the system determines if the probe image corresponds to a person on the watch list and then subsequently identifies the person through the match (assuming the identities of the watch list are known). It is therefore also a one-to-many problem but with an open-set assumption. When a probe is given to the system, the system compares it with the entire gallery (also known in this case as the watch list). If any match is above the operating threshold, an alarm will be triggered. If the top match is identified correctly, then the task was completed successfully. If, however the person in the probe image is not someone in the gallery and the alarm was nonetheless triggered, then it would be a false alarm (i.e., a false alarm occurs when the top match score for someone not in the watch list is above the operating threshold). If there is not an alarm then it might be that the probe is not in the gallery (a true negative) or that the system failed to recognize a person on the watch list (a false negative). The relationship between these different outcomes in the watch list task is indicated in Figure. (Introna & Nissenbaum, 2017)

Figure 8 Possible outcomes of watch list task (Introna & Nissenbaum, 2017)

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Face Representation It is the first task, that is, how to model a face. The way to represent a face determines the successive algorithms of detection and identification. For the entry-level recognition (that is, to determine whether or not the given image represents a face), a face category should be characterized by generic properties of all faces; and for the subordinate-level recognition (in other words, which face class the new face belongs to), detailed features of eyes, nose, and mouth have to be assigned to each individual face. There are a variety of approaches for face representation, which can be roughly classified into three categories: template-based, featurebased, and appearance-based. (Luo & Panitchob, 2017) The simplest template-matching approaches represent a whole face using a single template, i.e., a 2-D array of intensity, which is usually an edge map of the original face image. In a more complex way of template-matching, multiple templates may be used for each face to for recognition from different viewpoints. Another important variation is to employ a set of smaller facial feature templates that correspond to eyes, nose, and mouth, for a single viewpoint. The most attractive advantage of template-matching is the simplicity; however, it suffers from large memory requirement and inefficient matching. In feature-based approaches, geometric features, such as position and width of eyes, nose, and mouth, eyebrow's thickness and arches, face breadth, or invariant moments, are extracted to represent a face. Feature-based approaches have smaller memory requirement and a higher recognition speed than templatebased ones do. They are particularly useful for face scale normalization and 3D head modelbased pose estimation. However, perfect extraction of features is shown to be difficult in implementation. The idea of appearance-based approaches is to project face images onto a linear subspace of low dimensions. Such a subspace is first constructed by principal component analysis on a set of training images, with eigenfaces as its eigenvectors. Later, the concept of eigenfaces were extended to eigenfeatures, such as eigen eyes, eigen mouth, etc. for the detection of facial features. More recently, fisher face space and illumination subspace have been proposed for dealing with recognition under varying illumination. (Luo & Panitchob, 2017)

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Face Detection The first part of system is face detection. The output of the detection is the location of the face. Face detection techniques used in the literature are difficult to categorize, since most of the algorithms are grouping of methods for detecting faces to amplify the accuracy of the system. Detection techniques can be classified into two groups as Knowledge-Based techniques and Image-Based techniques. (Luo & Panitchob, 2017) Face detection is to locate a face in a given image and to separate it from the remaining scene. Several approaches have been proposed to fulfil the task. One of them is to utilize the elliptical structure of human head. This method locates the head outline by the Canny's edge finder and then fits an ellipse to mark the boundary between the head region and the background. However, this method is applicable only to frontal views, the detection of non-frontal views needs to be investigated. A second approach for face detection manipulates the images in “face space” . Images of faces do not change radically when projected into the face space, while projections of nonface images appear quite different. This basic idea used to detect the presence of faces in a scene: at every location in the image, calculate the distance between the local sub image and face space. This distance from face space is used as a measure of “faceness”, so the result of calculating the distance from face space at every point in the image is a “face map”. Low values, in other words, short distances from face space, in the face map indicate the presence of a face. (Luo & Panitchob, 2017)

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Face Detection Approaches Some of the main face detection methods are discussed here. Knowledge based methods are developed on the rules derived from the developer’s knowledge of human faces. Problem in this approach is the difficulty in translating human knowledge into well need rules. (Bhensle & Raja, 2017) Knowledge-based techniques used are Facial Features, Template Matching and Skin Color are used to locate eyes, mouth, nose or other facial features detect the faces. Skin Color or Facial Features are used to find eyes, nose, mouth and other facial features to detect the face. Color of the skin is dissimilar to other colors and it is unique. Its characteristics do not change with respect to pose variation. Face has unique features to differentiate from other objects and therefore, a pattern can be generated to check and detect faces. Facial features are essential information for individual faces and standard images can be generated in sequences. (Bhensle & Raja, 2017) Featured-based methods: Invariant features of faces are used for detecting texture, skin color. But features from such algorithm can be severely corrupted due to illumination, noise and occlusion. Template matching: Input image is compared with predefined face template. But the performance here suffers due to variations in scale, pose and shape. (Bhensle & Raja, 2017) Image-Based methods used training methods to make decision between face and non-face images. These methods used multiple images of face and non-face and trained to boost the accurateness of the system. Eigen Face, Neural Networks and SVM are different methods that are used face detection. (Bhensle & Raja, 2017) Appearance-based method: In template matching methods, the templates are predefined by experts. Whereas, the templates in appearance based methods are learned from examples in images. Statistical analysis and machine learning techniques can be used to find the relevant characteristics of face and non-face images. (Bhensle & Raja, 2017) Face Identification Face identification is performed at the subordinate-level. At this stage, a new face is compared to face models stored in a database and then classified to a known individual if a correspondence is found. The performance of face identification is affected by several factors: scale, pose, illumination, facial expression, and disguise. (Luo & Panitchob, 2017) The scale of a face can be handled by a rescaling process. In eigenface approach, the scaling factor can be determined by multiple trials. The idea is to use multiscale eigenfaces, in which PT1386619


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a test face image is compared with eigenfaces at a number of scales. In this case, the image will appear to be near face space of only the closest scaled eigenfaces. Equivalently, we can scale the test image to multiple sizes and use the scaling factor that results in the smallest distance to face space. (Luo & Panitchob, 2017) Varying poses result from the change of viewpoint or head orientation. Different identification algorithms illustrate different sensitivities to pose variation. (Luo & Panitchob, 2017) To identify faces in different illuminance conditions is a challenging problem for face recognition. The same person, with the same facial expression, and seen from the same viewpoint, can appear dramatically different as lighting condition changes. In recent years, two approaches, the fisher face space approach and the illumination subspace approach, have been proposed to handle different lighting conditions. The fisher face method projects face images onto a three-dimensional linear subspace based on Fisher's Linear Discriminant in an effort to maximize between-class scatter while minimize within-class scatter. The illumination subspace method constructs an illumination cone of a face from a set of images taken under unknown lighting conditions. This latter approach is reported to perform significantly better especially for extreme illumination. (Luo & Panitchob, 2017) Different from the effect of scale, pose, and illumination, facial expression can greatly change the geometry of a face. Attempts have been made in computer graphics to model the facial expressions from a muscular point of view. (Luo & Panitchob, 2017) Disguise is another problem encountered by face recognition in practice. Glasses, hairstyle, and makeup all change the appearance of a face. Most research work so far has only addressed the problem of glasses. (Luo & Panitchob, 2017) Methodology used for Face Recognition System Face recognition system generally recognizes the faces form single source, either from still images or recoded videos. In this work, we can recognize the face image from multiple sources like still images, webcams and recorded videos. Face detection unit can detect single face as well as multiple faces detect face from different sources. Face recognition module; match up face contender with face images which are stored in the database and recognize the face contender. (Bhensle & Raja, 2017)

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Figure 9 Methodologies for face recognition (Bhensle & Raja, 2017)

Face Recognition Mechanism Recognition as Part of Perception One possibility is to consider recognition as a part of perception. Arguably, recognition of simple features of a stimulus, or recognition that one stimulus differs from another, is really an aspect of perception. Perhaps we do not need to know anything about the world to recognize an emotion but are able to discriminate, categorize, and identify emotions solely on the basis of the geometric visual properties of a stimulus image. It is even conceivable (in principle) that such perceptual processing could be linked directly to language-related regions of the brain sufficient to produce the name of the emotion, in the absence of retrieving any other

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information associated with the stimulus (something akin to paired associate learning, for instance). (Singh, 2017) Recognition via the Generation of Associated Knowledge However, recognition typically involves more than just perceptual information. When we see a facial expression of fear, we can relate it not only to the percepts of other facial expressions in of its structure, but we can recognize that the person whose face we see is likely to scream, is likely to run away, has probably encountered something scary, and so on. None of that knowledge is present in the structure of the stimulus; it is present in our past experience with the world (and, to some limited extent, may even be present innately). A complex question concerns the precise mechanisms by which such knowledge might be retrieved. In general, the knowledge is not stored in any explicit format but rather relies on recipes for reconstructing knowledge by reactivation of the representations that were originally associated with one another when the knowledge was acquired (A. R. Damasio & Damasio, 1994). The simplest example of such a mechanism would be literal association, as when we see a face of fear and hear a scream at the same time and link the two henceforth in memory. In general, linking other knowledge with a perception of the facial expression will be vastly more complex and will rely on multiple higher order associations that may be fairly separated in time (e.g., seeing a face of fear and seeing the chasing tiger sometime later), as well as on symbolic representations that, in humans, rely substantially on language (e.g., seeing a face of fear and merely being told that the person was afraid because he or she was running away from a tiger). (Singh, 2017) The general neural scheme for implementing the above mechanisms requires the binding of information between separate neural representations so that they can be processed as components of knowledge about the same concept. In the perceptual case, a stimulus activates multiple neural regions that represent particular aspects of its visual properties, and the coherent ensemble of these different bits of knowledge (the representations of the different properties of the stimulus) constitutes the perceptual mechanism. But, this mechanism can be extended beyond those neural regions that represent the visual properties of the stimulus to include those that represent knowledge not of the stimulus itself but of that with which it has been associated. The demand for integrating neural representations that are spatially separated in the brain would require extensive connections as well as feedforward connections between different neural regions. One might thus envision a continuous dynamic interplay between

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feedforward, , and horizontal information flow from which the brain constructs representations of visual stimuli. (Lamme, Super, & Spekreijse, 1998) Schemes such as Ullman’s (1995) “counter streams” or Edelman’s (1987) “re-entry” both capture this idea: The representation of the stimulus itself, and of its associated knowledge, evolves contemporaneously such that the one continuously modulates the other and perception and recognition become parts of the same large-scale process. (Singh, 2017) Recognition via the Generation of a Simulation The above mechanisms, although they rightly can be considered creative, are relatively direct: On linking together the various representations that give rise to components of the conceptual knowledge about the emotion that is signalled by the stimulus, the subject has available all the information necessary to recognize the emotion; all that is required to perform most recognition tasks now are an implementation of the reconstructed conceptual knowledge in of language so that the subject can tell us what he or she knows. But there are less direct routes that might come into play also. It may be that the explicit knowledge triggered in the above scheme is insufficient to recognize an emotion, perhaps because that particular emotion was never seen before or because the recipe for reconstructing knowledge about it provides insufficient detail. Another mechanism might attempt to generate conceptual knowledge using an inverse mapping that seeks to trigger those states normally antecedent to producing the facial expression. Such a mechanism would attempt to simulate in the observer the state of the person shown in the stimulus by estimating the motor representations that gave rise to the observed stimulus. Once the observer has generated the state that the other person is presumed to share, a representation of this actual state in the observer could in turn trigger conceptual knowledge. Simulation thus still requires the triggering of conceptual knowledge, but the basis of the trigger is not a representation of someone else but rather a representation of ourselves (simulating the other person). The simulation hypothesis has recently received considerable attention due to experimental findings that appear to it. In the premotor cortex of monkeys, Rizzolatti and colleagues have reported neurons that respond not only when the monkey prepares to perform an action itself but also when the monkey observes the same visually presented action performed by someone else. (Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Gallese & Goldman, 1999; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). Various ive findings have also been obtained in humans: Observing another’s actions results in desynchronization in motor cortex as measured with Adolph. It thus appears that

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primates construct motor representations suited to performing the same action that they visually perceive someone else perform, in line with the simulation theory. (Singh, 2017) Face recognition algorithms There are two predominant approaches to the face recognition problem: Geometric (feature based) and photometric (view based). As developer interest in face recognition continued, many different algorithms were developed, three of which have been well studied in face recognition literature. Recognition algorithms can be divided into two main approaches: Geometric is based on geometrical relationship between facial landmarks, or in other words the spatial configuration of facial features. That means that the main geometrical features of the face such as the eyes, nose and mouth are first located and then faces are classified on the basis of various geometrical distances and angles between features. (Figure )

Photometric stereo is used to recover the shape of an object from a number of images taken under different lighting conditions. The shape of the recovered object is defined by a gradient map, which is made up of an array of surface normal. (Zhao and Chellappa, 2006) (Figure ).

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Popular recognition algorithms include: 1. Principal Component Analysis using Eigen faces, (PCA) 2. Linear Discriminate Analysis, 3. Elastic Bunch Graph Matching using the Fisher face algorithm The early work in face recognition was based on the geometrical relationships between facial landmarks as a means to capture and extract facial features. This method is obviously highly dependent on the detection of these landmarks (which may be very difficult is variations in illumination, especially shadows) as well as the stability of these relationships across pose variation. These problems were and still remain significant stumbling blocks for face detection and recognition. This work was followed by a different approach in which the face was treated as a general pattern with the application of more general pattern recognition approaches, which are based on photometric characteristics of the image. These two starting points: geometry and the photometric approach are still the basic starting points for developers of facial recognition algorithms. To implement these approaches a huge variety of algorithms have been developed. Here we will highlight three of the most significant streams of work: Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), and Elastic Bunch Graph Matching (EBGM). Principal Components Analysis (PCA) PCA, commonly referred to as the use of eigenfaces, is the technique pioneered by Kirby and Sirivich in 1988. With PCA, the probe and gallery images must be the same size and must first be normalized to line up the eyes and mouth of the subjects within the images. The PCA approach is then used to reduce the dimension of the data by means of data compression basics2 and reveals the most effective low dimensional structure of facial patterns. This reduction in dimensions removes information that is not useful4 and precisely decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. A probe image is compared against a gallery image by measuring the distance between their respective feature vectors. The PCA approach typically requires the full-frontal face to be presented each time; otherwise the image results in poor performance. The primary advantage of this technique is that it can reduce the data needed to identify the individual to 1/1000th of the data presented. The PCA technique converts each two-dimensional image into a one-dimensional vector. This vector is then decomposed into orthogonal (uncorrelated) principle components (known as PT1386619


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eigenfaces) in other words, the technique selects the features of the image (or face) which vary the most from the rest of the image. In the process of decomposition, a large amount of data is discarded as not containing significant information since 90% of the total variance in the face is contained in 5-10% of the components. This means that the data needed to identify an individual is a fraction of the data presented in the image. Each face image is represented as a weighted sum (feature vector) of the principle components (or eigenfaces), which are stored in a one-dimensional array. Each component (eigenface) represents only a certain feature of the face, which may or may not be present in the original image. A probe image is compared against a gallery image by measuring the distance between their respective feature vectors. For PCA to work well the probe image must be similar to the gallery image in of size (or scale), pose, and illumination. It is generally true that PCA is reasonably sensitive to scale variation. (Introna & Nissenbaum, 2017) Linear Discriminant Analysis (LDA) LDA is a statistical approach for classifying samples of unknown classes based on training samples with known classes. This technique aims to maximize between-class (i.e., across s) variance and minimize within-class (i.e., within ) variance. In Figure 2 where each block represents a class, there are large variances between classes, but little variance within classes. When dealing with high dimensional face data, this technique faces the small sample size problem that arises where there are a small number of available training samples compared to the dimensionality of the sample space. LDA is a statistical approach based on the same statistical principles as PCA. LDA classifies faces of unknown individuals based on a set of training images of known individuals. The technique finds the underlying vectors in the facial feature space (vectors) that would maximize the variance between individuals (or classes) and minimize the variance within a number of samples of the same person (i.e., within a class). If this can be achieved, then the algorithm would be able to discriminate between individuals and yet still recognize individuals in some varying conditions (minor variations in expression, rotation, illumination, etc.). If we look at Figure 8 we can see that there is a relatively large amount of variation between the individuals and small variations between the varieties of poses of the same individual. To do this the algorithm must have an appropriate training set. The database should contain several examples of face images for each subject in the training set and at least one example in the test set. These examples should represent different frontal views of subjects with minor variations in view angle. They should also include different facial expressions, different lighting and background PT1386619


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conditions, also examples with and without glasses if appropriate. Obviously, an increase in the number of varying samples of the same person will allow the algorithm to optimize the variance between classes and therefore become more accurate. This may be a serious limitation in some contexts (also known as the small sample size problem). As for PCA, LDA works well if the probe image is relatively similar to the gallery image in of size, pose, and illumination. With a good variety in sampling this can be somewhat varied, but only up to a point. For more significant variation other non-linear approaches are necessary. (Introna & Nissenbaum, 2017)

Figure 10 Example of variation between and within classes (Introna & Nissenbaum, 2017)

Elastic Bunch Graph Matching (EBGM) EBGM relies on the concept that real face images have many nonlinear characteristics that are not addressed by the linear analysis methods discussed earlier, such as variations in illumination (outdoor lighting vs. indoor fluorescents), pose (standing straight vs. leaning over) and expression (smile vs. frown). A Gabor wavelet transform creates a dynamic link architecture that projects the face onto an elastic grid. The Gabor jet is a node on the elastic grid, notated by circles on the image below, which describes the image behaviour around a given pixel. It is the result of a convolution of the image with a Gabor filter, which is used to detect shapes and to extract features using image processing. [A convolution expresses the amount of overlap from functions, blending the functions together.] Recognition is based on the similarity of the Gabor filter response at each Gabor node.4 This biologically-based method using Gabor filters is a process executed in the visual cortex of higher mammals. The difficulty with this method is the requirement of accurate landmark localization, which can sometimes be achieved by combining PCA and LDA methods. EBGM relies on the concept that real face images have many nonlinear characteristics that are not addressed by the linear analysis methods such as PCA and LDA—such as variations in illumination, pose, and expression. The EBGM method places small blocks of numbers (called “Gabor filters”) over small areas of the image, multiplying and adding the blocks with the pixel PT1386619


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values to produce numbers (referred to as “jets”) at various locations on the image. These locations can then be adjusted to accommodate minor variations. The success of Gabor filters is in the fact that they remove most of the variability in images due to variation in lighting and contrast. At the same time, they are robust against small shifts and deformations. The Gabor filter representation increases the dimensions of the feature space (especially in places around key landmarks on the face such as the eyes, nose, and mouth) such that salient features can effectively be discriminated. This new technique has greatly enhanced facial recognition performance under variations of pose, angle, and expression. New techniques for illumination normalization also enhance significantly the discriminating ability of the Gabor filters. (Introna & Nissenbaum, 2017) Facial recognition algorithms Steps in the facial recognition process Let us for the moment assume that we have a probe image with which to work. The facial recognition process normally has four interrelated phases or steps. The first step is face detection, the second is normalization, the third is feature extraction, and the final cumulative step is face recognition. These steps depend on each other and often use similar techniques. They may also be described as separate components of a typical FRS. Nevertheless, it is useful to keep them conceptually separate for the purposes of clarity. Each of these steps poses very significant challenges to the successful operation of a FRS. Figure indicates the logical sequence of the different steps. (Introna & Nissenbaum, 2017) Detecting a face: Detecting a face in a probe image may be a relatively simple task for humans, but it is not so for a computer. The computer has to decide which pixels in the image is part of the face and which are not. In a typical port photo, where the background is clear, it is easy to do, but as soon as the background becomes cluttered with other objects, the problem becomes extremely complex. Traditionally, methods that focus on facial landmarks (such as eyes), that detect face-like colors in circular regions, or that use standard feature templates, were used to detect faces. (Introna & Nissenbaum, 2017) Normalization: Once the face has been detected (separated from its background), the face needs to be normalized. This means that the image must be standardized in of size, pose, illumination, etc., relative to the images in the gallery or reference database. To normalize a probe image, the key facial landmarks must be located accurately. Using these landmarks, the normalization algorithm can (to some degree) reorient the image for slight variations. Such corrections are, however, based on statistical inferences or approximations which may not be PT1386619


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entirely accurate. Thus, it is essential that the probe is as close as possible to a standardized face. Facial landmarks are the key to all systems, irrespective of the overall method of recognition. If the facial landmarks cannot be located, then the recognition process will fail. Recognition can only succeed if the probe image and the gallery images are the same in of pose orientation, rotation, scale, size, etc. Normalization ensures that this similarity is achieved to a greater or lesser degree. (Introna & Nissenbaum, 2017)

Figure 11 Steps in facial recognition process (Introna & Nissenbaum, 2017)

Feature extraction and recognition: Once the face image has been normalized, the feature extraction and recognition of the face can take place. In feature extraction, a mathematical representation called a biometric template or biometric reference is generated, which is stored in the database and will form the basis of any recognition task. Facial recognition algorithms differ in the way they translate or transform a face image (represented at this point as grayscale pixels) into a simplified mathematical representation (the “features”) in order to perform the recognition task. It is important for successful recognition that maximal information is retained in this transformation process so that the biometric template is sufficiently distinctive. If this cannot be achieved, the algorithm will not have the discriminating ability required for successful recognition. The problem of biometric templates from different individuals being insufficiently distinctive (or too close to each other) is often referred to as the generation of biometric doubles. It is in this process of mathematical transformation (feature extraction) and matching (recognition) of a biometric template that particular algorithms differ significantly in their approach. It is beyond the scope of this report to deal with these approaches in detail. (Introna & Nissenbaum, 2017) Conclusion

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CHAPTER 6 PRINCIPAL COMPONENT ANALYSIS Introduction Principal component analysis (PCA) was invented in 1901 by Karl Pearson. PCA is a variable reduction procedure and useful when obtained data have some redundancy. This will result into reduction of variables into smaller number of variables which are called Principal Components which will for the most of the variance in the observed variable. Problems arise when we wish to perform recognition in a high-dimensional space. Goal of PCA is to reduce the dimensionality of the data by retaining as much as variation possible in our original data set. On the other hand, dimensionality reduction implies information loss. The best low-dimensional space can be determined by best principal components. The major advantage of PCA is using it in eigenface approach which helps in reducing the size of the database for recognition of a test images. The images are stored as their feature vectors in the database which are found out projecting each and every trained image to the set of Eigen faces obtained. PCA is applied on Eigen face approach to reduce the dimensionality of a large data set. (Singh & Kumar, 2017) One of the simplest and most effective PCA approaches used in face recognition systems is the so-called eigenface approach. This approach transforms faces into a small set of essential characteristics, eigenfaces, which are the main components of the initial set of learning images (training set). Recognition is done by projecting a new image in the eigenface subspace, after which the person is classified by comparing its position in eigenface space with the position of known individuals. The advantage of this approach over other face recognition systems is in its simplicity, speed and insensitivity to small or gradual changes on the face. The problem is limited to files that can be used to recognize the face. Namely, the images must be vertical frontal views of human faces. The whole recognition process involves two steps: 1. Initialization process 2. Recognition process The Initialization process involves the following operations 1. Acquire the initial set of face images called as training set. 2. Calculate the Eigenfaces from the training set, keeping only the highest eigenvalues. These M images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated.

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3. Calculate distribution in this M-dimensional space for each known person by projecting his or her face images onto this face-space. These operations can be performed from time to time whenever there is a free excess operational capacity. This data can be cached which can be used in the further steps eliminating the overhead of re-initializing, decreasing execution time thereby increasing the performance of the entire system. Having initialized the system, the next process involves the steps: 1. Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the Eigenfaces. 2. Determine if the image is a face at all (known or unknown) by checking to see if the image is sufficiently close to a ―free space‖. 3. If it is a face, then classify the weight pattern as either a known person or as unknown. 4. Update the eigenfaces or weights as either a known or unknown, if the same unknown person face is seen several times then calculate the characteristic weight pattern and incorporate into known faces. The last step is not usually a requirement of every system and hence the steps are left optional and can be implemented as when the there is a requirement. (Paul & Sumam, 2017) Eigen Face Approach In the language of information theory, the relevant information in a face needs to be extracted, encoded efficiently and one face encoding is compared with the similarly encoded database. The trick behind extracting such kind of information is to capture as many variations as possible from the set of training images. Mathematically, the principal components of the distribution of faces are found out using the eigenface approach. First the eigenvectors of the covariance matrix of the set of face images is found out and then they are sorted according to their corresponding eigenvalues. Then a threshold eigenvalue is taken into and eigenvectors with eigenvalues less than that threshold values are discarded. So ultimately the eigenvectors having the most significant eigenvalues are selected. Then the set of face images are projected into the significant eigenvectors to obtain a set called eigenfaces. Every face has a contribution to the eigenfaces obtained. The best M eigenfaces from a M dimensional subspace is called “face space”.

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Each individual face can be represented exactly as the linear combination of “eigenfaces” or each face can also be approximated using those significant eigenfaces obtained using the most significant eigen values. (Subudhi & Mishra, 2017) Eigen Values and Eigen Vectors In linear algebra, the eigenvectors of a linear operator are non-zero vectors which, when operated by the operator, result in a scalar multiple of them. Scalar is then called Eigen value (λ) associated with the eigenvector (X). Eigen vector is a vector that is scaled by linear transformation. It is a property of matrix. When a matrix acts on it, only the vector magnitude is changed not the direction. AX = λX, where A is a vector function. (A − λI)X = 0, where I is the identity matrix. This is a homogeneous system of equations and form fundamental linear algebra. We know a non-trivial solution exists if and only if Det(A − λI) = 0, where det denotes determinant. When evaluated becomes a polynomial of degree n. This is called characteristic polynomial of A. If A is N by N then there are n solutions or n roots of the characteristic polynomial. Thus, there are n Eigen values of A satisfying the equation. AXi = λiXi , where i = 1,2,3,.....n If the Eigen values are all distinct, there are n associated linearly independent eigenvectors, whose directions are unique, which span an n dimensional Euclidean space. (Singh & Kumar, 2017) Face Image Representation Training set of m images of size N*N are represented by vectors of size N2. Each face is represented by Γ1, Γ2, Γ3, ΓM. Feature vector of a face is stored in a N×N matrix. Now, this two-dimensional vector is changed to one dimensional vector. For example 1 1 2 2 [ ]=[ ] 2 1 2 1 Each face image is represented by the vector Γi.

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1 1 2 1 −2 1 3 2 Γ1 = [ ], Γ2 = [ ], , Γ3 = [ ]………………..., ΓM = [ ] 1 −2 −1 2 −3 2 3 1 (Singh & Kumar, 2017) Mean and Mean Centered Images Average face image is calculated by 𝑀

Ψ = (1/𝑀) ∑ Γ𝑖 𝑖=1

1 1 2 1 −1 −2 1 −1 3 2 [ ] + [ ] + [ ] + … … …….+ [ ]→[ ] 1 −2 2 −1 2 −3 2 3 1 −3 Each face differs from the average by Φ𝑖 = Γ𝑖 − Ψ which is called mean centered image. 2 2 3 2 −1 4 2 3 Φ1 = [ ] Φ2 = [ ] Φ3 = [ ] … … … … … Φ𝑀 = [ ] −1 −3 −4 0 0 5 6 4 (Singh & Kumar, 2017) Covariance Matrix A covariance matrix is constructed as: C = AAT, where A = [Φ1 , Φ2 , , Φ𝑀 ] of size N2 × N2. 2 A = [−1 −1 0

3 −2] AT = [2 1 3 2

−1 −1 0 ] −2 1 2

Size of covariance matrix will be N2 × N2 (4*4 in this case) Eigen vectors corresponding to this covariance matrix is needed to be calculated, but that will be a tedious task therefore, For simplicity, we calculate ATA which would be a 2 * 2 matrix in this case. 6 7 ATA= [ ] size of this matrix is M * M 7 18 Consider the eigenvectors Vi of AT A such that ATAXi = λiXi The eigenvectors vi of ATA are X1 and X2 which are 2 × 1. Now multiplying the above equation with A both sides we getAATAXi = AλiXi

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AAT(AXi) = λi(AXi) Eigen vectors corresponding to AAT can now be easily calculated now with reduced dimensionality where AXi is the Eigen vector and λi is the Eigen value. (Singh & Kumar, 2017) Eigen Face Shape The Eigen vectors of the covariance matrix AAT are AXi which is denoted by Ui. Ui resembles facial images which look ghostly and are called Eigen faces. Eigen vectors correspond to each Eigen face in the face space and discard the faces for which Eigen values are zero thus reducing the Eigen face space to an extent. The Eigen faces are ranked according to their usefulness in characterizing the variation among the images. A face image can be projected into this face space by Ωk = UT(Γk − Ψ); k=1,....,M, where (ΓkΨ) is the mean centered image. Hence projection of each image can be obtained as Ω1 for projection of image1 and Ω2 for projection of image and hence forth. (Singh & Kumar, 2017)

Face Recognition Step The test image, Γ, is projected into the face space to obtain a vector, Ω as Ω = UT(Γ − Ψ) The distance of Ω to each face is called Euclidean distance and defined by ; k = 1, M where Ωk is a vector describing the kth face class. A face is classified as belonging to class k when the minimum k is below some chosen threshold Θc. otherwise the face is classified as unknown. Θc, is half the largest distance between any two face images: Θc = (1/2)maxj,k ||Ωj − Ωk||; j,k = 1,.....,M We have to find the distance between the original test image Γ and its reconstructed image from the Eigen face Γf , where Γf = U ∗ Ω + Ψ If

then input image is not even a face image and not recognized.

If

and

Θ for all k then input image is a face image but it is recognized as unknown

face.

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If

and

Θ for all k then input images are the individual face image associated with

the class vector Ωk. (Singh & Kumar, 2017) Summarizing the above math, the approach for face recognition is as follows 1. Acquire an initial set of N face images (training images). 2. Calculate the eigenface from the training set keeping only the M images that correspond to the highest eigenvalues. These M images define the “facespace”. As new faces are encountered, the “eigenfaces” can be updated or recalculated accordingly. 3. Calculate the corresponding distribution in M dimensional weight space for each known individual by projecting their face images onto the “face space”. 4. Calculate a set of weights projecting the input image to the M “eigenfaces”. 5. Determine whether the image is a face or not by checking the closeness of the image to the “face space”. 6. If it is close enough, classify, the weight pattern as either a known person or as an unknown based on the Euclidean distance measured. 7. If it is close enough then cite the recognition successful and provide relevant information about the recognized face form the database which contains information about the faces.

Figure 12 Flow chart for PCA algorithm

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Conclusion

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CHAPTER 7 COMPONENT DISCRIPTION AND COMPARATIVE ANALYSIS Components used for developing the prototype 1. Arduino UNO 2. RFID EM 18 Module 3. Servo Motor 4. USB type 2.0 5. LED 6. LCD 7. Atmega 328 Arduino UNO It is basically a microcontroller board which contains on-board power supply, USB port to communicate with PC, and an Atmel microcontroller chip. It simplifies the process of creating any control system by providing the standard board that can be programmed and connected to the system without the need to any sophisticated PCB design and implementation. It is an open source hardware, anyone can get the details of its design and modify it or make his own one himself.

Arduino Uno is a microcontroller board based on the ATmega328P. It has 14 digital input/output pins among which 6 can be used as PWM outputs, 6 anaputs, a 16 MHz quartz crystal, a USB connection, a power jack, an ICSP header and a reset button. It contains everything needed to the microcontroller which is simply connected to a computer with a USB cable or power it with an AC-to-DC adapter or battery to get started. The UNO can be tinkered without worrying too much about doing something wrong, worst case scenario you can replace the chip for a few dollars and start over again. "Uno" means one in Italian and was chosen to mark the release of Arduino Software (IDE) 1.0. The Uno board and version 1.0 of Arduino Software (IDE) were the reference versions of Arduino, now evolved to newer releases. The Uno board is the first in a series of USB Arduino PT1386619


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boards, and the reference model for the Arduino platform; for an extensive list of current, past or outdated boards see the Arduino index of boards. (Walker, 2016) Programming The Arduino Uno can be programmed with the (Arduino Software (IDE)). Select "Arduino Uno from the Tools > Board menu (according to the microcontroller on your board). The ATmega328 on the Arduino Uno comes pre-programmed with a boot loader that allows to new code to it without the use of an external hardware programmer. It communicates using the original STK500 protocol. The boot loader and program the microcontroller can be byed through the ICSP (In-Circuit Serial Programming) header using Arduino ISP or similar. The ATmega16U2 (or 8U2 in the rev1 and rev2 boards) firmware source code is available in the Arduino repository. The ATmega16U2/8U2 is loaded with a DFU boot loader, which can be activated by: On Rev1 boards in which it can be activated by connecting the solder jumper on the back of the board (near the map of Italy) and then reseing the 8U2 and On Rev2 or later boards in which there is a resistor that pulling the 8U2/16U2 HWB line to ground, making it easier to put into DFU mode. Atmel’s FLIP software (Windows) or the DFU Programmer (Mac OS X and Linux) can be used to load a new firmware or in other words it can be stated that with the use of the ISP header with an external programmer (overwriting the DFU boot loader). (Walker, 2016) SPECIFICATIONS Microcontroller

ATmega 328p

Operating Voltage

5V

Input Voltage (recommended)

7-12V

Input Voltage (limit)

6-20V

Digital I/O Pins

14 (of which 6 provide PWM output)

PWM Digital I/O Pins

6

Anaput Pins

6

DC Current per I/O Pin

20 mA

DC Current for 3.3V Pin

50 mA

Flash Memory SRAM

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EEPROM

1 KB (ATmega328P)

Clock Speed

16 MHz

LED_BUILTIN

13

Length

68.6 mm

Power The Arduino Uno board can be powered via the USB connection or with an external power supply. The power source is selected automatically in which the External power can come either from an AC-to-DC adapter or battery. The adapter can be connected by plugging a 2.1mm center-positive plug into the board's power jack. Leads from a battery can be inserted in the GND and Vin pin headers of the POWER connector. The board can operate on an external supply from 6 to 20 volts. If supplied with less than 7V, however, the 5V pin may supply less than five volts and the board may become unstable. If using more than 12V, the voltage regulator may overheat and damage the board. The recommended range is 7 to 12 volts. The power pins of the module have been mentioned which are as follows: 1. Vin: - The input voltage to the Arduino board when it's using an external power source (as opposed to 5 volts from the USB connection or other regulated power source). You can supply voltage through this pin or if supplying voltage via the power jack, access it through this pin. 2. 5V: - This pin outputs a regulated 5V from the regulator on the board. The board can be supplied with power either from the DC power jack (7 - 12V), the USB connector (5V), or the VIN pin of the board (7-12V). Supplying voltage via the 5V or 3.3V pins byes the regulator, and can damage your board. We don't advise it. 3. 3V3. A 3.3-volt supply generated by the on-board regulator. Maximum current draw is 50 mA. 4. GND. Ground pins. 5. IOREF. This pin on the Arduino board provides the voltage reference with which the microcontroller operates. A properly configured shield can read the IOREF pin voltage and select the appropriate power source or enable voltage translators on the outputs to work with the 5V or 3.3V. (Walker, 2016) Memory The ATmega328 has 32 KB (with 0.5 KB occupied by the boot loader). It also has 2 KB of SRAM and 1 KB of EEPROM (which can be read and written with the EEPROM library). PT1386619


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Communication Arduino Uno has a number of methods for communicating with a computer, with another Arduino board, or with other microcontrollers. The ATmega328 provides UART TTL (5V) serial communication, which is available on digital pins 0 (RX) and 1 (TX). An ATmega16U2 on the board channels this serial communication over USB and appears as a virtual com port to software on the computer. The 16U2 firmware uses the standard USB COM drivers, and no external driver is needed. However, on Windows, a .inf file is required. The Arduino Software (IDE) includes a serial monitor which allows simple textual data to be sent to and from the board. The RX and TX LEDs on the board will flash when data is being transmitted via the USB-to-serial chip and USB connection to the computer (but not for serial communication on pins 0 and 1). A Software Serial library allows serial communication on any of the Uno's digital pins. The ATmega328 also s I2C (TWI) and SPI communication. The Arduino Software (IDE) includes a Wire library to simplify use of the I2C bus and For SPI communication it uses the SPI library. (Walker, 2016) RFID EM 18 Module Introduction This is a low frequency (125Khz) RFID reader with serial output with a range of 8-12cm. It is a compact unit with built in antenna and can be directly connected to the PC.

Working Many types of RFID exist, but at the highest level, we can divide RFID devices into two classes: active and ive. Active tags require a power source they’re either connected to a powered infrastructure or use energy stored in an integrated battery. In the latter case, a tag’s lifetime is limited by the stored

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energy, balanced against the number of read operations the device must undergo. One example of an active tag is the transponder attached to an aircraft that identifies its national origin. Another example is a LoJack device attached to a car, which incorporates cellular technology and a GPS to locate the car if stolen. However, batteries make the cost, size, and life-time of active tags impractical for the retail trade. ive RFID is of interest because the tags don’t require batteries or maintenance. The tags also have an indefinite operational life and are small enough to fit into a practical adhesive label. A ive tag consists of three parts: an antenna, a semiconductor chip attached to the antenna, and some form of encapsulation. The tag reader is responsible for powering and communicating with a tag. The tag antenna captures energy and transfers the tag’s ID (the tag’s chip coordinates this process). The encapsulation maintains the tag’s integrity and protects the antenna and chip from environmental conditions or reagents. The encapsulation could be a small glass vial or a laminar plastic substrate with adhesive on one side to enable easy attachment to goods. Two fundamentally different RFID design approaches exist for transferring power from the reader to the tag: magnetic induction and electromagnetic (EM) wave capture. These two designs take advantage of the EM properties associated with an RF antenna—the near field and the far field. Both can transfer enough power to a remote tag to sustain its operation—typically between 10 W and 1 mW, depending on the tag type. (For comparison, the nominal power an Intel X Scale processor consumes is approximately 500 mW, and an Intel Pentium 4 consumes up to 50 W.) Through various modulation techniques, near- and far-fieldbased signals can also transmit. Specification 1.

5VDC through USB (External 5V supply will boost range of the module)

2.

Current: <50mA

3.

Operating Frequency: 125Khz

4.

Read Distance: 10cm

5.

Size of RFID reader module: 32mm(length) * 32mm(width) * 8mm(height). (HAREENDRAN, 2017)

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Servo Motor A servomotor is a rotary actuator or linear actuator that allows for precise control of angular or linear position, velocity and acceleration. It consists of a suitable motor coupled to a sensor for position . It also requires a relatively sophisticated controller, often a dedicated module designed specifically for use with servomotors. Servomotors are not a specific class of motor although the term servomotor is often used to refer to a motor suitable for use in a closed-loop control system. Servomotors are used in applications such as robotics, CNC machinery or automated manufacturing. What’s inside the servo? To fully understand how the servo works, you need to take a look under the hood. Inside there is a pretty simple set-up: a small DC motor, potentiometer, and a control circuit. The motor is attached by gears to the control wheel. As the motor rotates, the potentiometer's resistance changes, so the control circuit can precisely regulate how much movement there is and in which direction. When the shaft of the motor is at the desired position, power supplied to the motor is stopped. If not, the motor is turned in the appropriate direction. The desired position is sent via electrical pulses through the signal wire. The motor's speed is proportional to the difference between its actual position and desired position. So, if the motor is near the desired position, it will turn slowly, otherwise it will turn fast. This is called proportional control. This means the motor will only run as hard as necessary to accomplish the task at hand, a very efficient.

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How is the servo controlled?

Servos are controlled by sending an electrical pulse of variable width, or pulse width modulation (PWM), through the control wire. There is a minimum pulse, a maximum pulse, and a repetition rate. A servo motor can usually only turn 90 degrees in either direction for a total of 180-degree movement. The motor's neutral position is defined as the position where the servo has the same amount of potential rotation in the both the clockwise or counterclockwise direction. The PWM sent to the motor determines position of the shaft, and based on the duration of the pulse sent via the control wire; the rotor will turn to the desired position. The servo motor expects to see a pulse every 20 milliseconds (ms) and the length of the pulse will determine how far the motor turns. For example, a 1.5ms pulse will make the motor turn to the 90-degree position. Shorter than 1.5ms moves it to 0 degrees, and any longer than 1.5ms will turn the servo to 180 degrees.

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When these servos are commanded to move, they will move to the position and hold that position. If an external force pushes against the servo while the servo is holding a position, the servo will resist from moving out of that position. The maximum amount of force the servo can exert is called the torque rating of the servo. Servos will not hold their position forever though; the position pulse must be repeated to instruct the servo to stay in position. Types of Servo Motors: There are two types of servo motors - AC and DC. AC servo can handle higher current surges and tend to be used in industrial machinery. DC servos are not designed for high current surges and are usually better suited for smaller applications. Generally speaking, DC motors are less expensive than their AC counterparts. These are also servo motors that have been built specifically for continuous rotation, making it an easy way to get your robot moving. They feature two ball bearings on the output shaft for reduced friction and easy access to the restpoint adjustment potentiometer. Servo Motors Applications: Servos are used in radio-controlled airplanes to position control surfaces like elevators, rudders, walking a robot, or operating grippers. Servo motors are small, have built-in control circuitry and have good power for their size. In food services and pharmaceuticals, the tools are designed to be used in harsher environments, where the potential for corrosion is high due to being washed at high pressures and temperatures repeatedly to maintain strict hygiene standards. Servos are also used in inline manufacturing, where high repetition yet precise work is necessary. USB type 2.0 This cord is a standard USB 2.0 Cable with a Type-A Male USB Connector at one end and a Mini Type-B Male USB Connector at the other end. These fully rated universal serial bus cables provide transfer rates up to 480Mbps, depending on USB version, and easily attach to any mini-USB device. Mini USB cables are designed to connect from your USB port on Hub, PC or Mac to your USB device, including cellular phones, PDAs and Transcend AP units. Foil and braid shielding reduces EMI/RFI interference, and twisted-pair construction helps reduce cross talk thus ensuring high-speed, error-free data transfer. Meets all the UL requirements for communication cabling, and is compatible with USB 1.0, 1.1, and 2.0.

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LED LEDs, or light–emitting diodes, are semiconductor devices that produce visible light when an electrical current is ed through them. LEDs are a type of Solid State Lighting (SSL), as are organic light–emitting diodes (OLEDs) and light–emitting polymers (LEPs). A LED is a component that only allows electricity to flow one way. It can be thought as a sort of one way street for electrons. Because of this characteristic diode are used to transform or rectify AC voltage into a DC voltage. Diodes have two connections an anode and a cathode. The cathode is the end on the schematic with the point of the triangle pointing towards a line. The anode is of course the opposite end. Current flows from the anode to the cathode. Light emitting diodes or LEDs differ from regular diodes in that when a voltage is applied they emit light. LEDs are used as indicators transmitters etc. Most likely a LED will never burn out like a regular lamp will and requires many times less current. Because LED act like regular diodes and will from a short if connected positive and negative. Visible light-emitting diodes (VLEDs), such as the ones that light up numbers in a digital clock, are made of materials characterized by a wider gap between the conduction band and the lower orbitals. The size of the gap determines the frequency of the photon -- in other words, it determines the colure of the light. While LEDs are used in everything from remote controls to the digital displays on electronics, visible LEDs are growing in popularity and use thanks to their long lifetimes and miniature size. Depending on the materials used in LEDs, they can be built to shine in infrared, ultraviolet, and all the colures of the visible spectrum in between.

Figure 20: LED

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LCD A liquid-crystal display (LCD) is a flat display, electronic visual display, or video display that uses the light modulating properties of liquid crystals. Liquid crystals do not emit light directly. 16x2 LCD has 2 horizontal line which comprising a space of 16 displaying character.

It has two type of inbuilt that is Command : - Command is used to insert a special command into the LCD. Data : - While Data is used to insert a data into the LCD. Command is a special set of data which is used to give the internal command to LCD like Clear screen, move to line 1 character 1, setting up the cursor etc. LCD stands for the Liquid crystal display. The LCD screen is an electronic display component and it is found in a comprehensive range of applications. The 16x2 LCD display is very simple components and is very generally used in various strategies and circuits. This component is preferred concluded seven sections and other multi section LEDs. The reasons being LCDs are low-cost, simply Programmable, have no limitation of showing special & even custom characters (Unlike in seven segments), moving picture and so on. A 16x2 LCD means it can display 16 characters per line and there are 2 such lines. Or it can be defined as it has 16 columns and two rows. In this LCD each character is displayed in 5x7 pixel matrix. This LCD has two s, namely, Command and Data. The present command stores the command instructions given to the LCD by microcontroller or other components. A command is an instruction given to LCD to do a predefined task like initializing it, clearing its screen, setting the cursor position, controlling display etc. The data stores the data to be displayed on the LCD. The data is the ASCII (American standard Code for Information Interchange) value of the character to be displayed on the LCD. FEATURES:  5 x 8 dots with cursor  Built-in controller PT1386619


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 + 5V power supply (Also available for + 3V)  White LED Backlight The sequence for writing to the LCD is: To activate, E is low Select the to write to by setting RS high (data) or low (control) Write the eight bits of data to the LCD Set the Enable signal high then low again Pin Diagram of 16x2 LCD

Sr. No

Pin No.

Pin Description

1

Pin 1 (GND)

This is a ground pin to apply a ground to LCD.

2

Pin 2 (VCC)

This is the supply voltage pin to apply voltage to LCD.

3

Pin 3 (VEE)

This is the pin for adjusting a contrast of the LCD display by attaching a veriable resistor in between VCC and GND.

4

Pin 4 (RS)

RS stands for Select. This pin is used to select command/data . If RS=0 then command is selected. If RS=1 then data is selected.

5

Pin 5 (R/W)

R/W stands for Read/Write. This pin is used to select the operation Read/Write. If R/W=0 then Write operation is performed. If R/W=1 then Read operation is performed.

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6

Pin 6 (EN)

En stand for Enable signal. A positive going pulse on this pin will perform a read/write function to the LCD.

7

Pin

7-14

(DB0- This 8 pin is used as a Data pin of LCD.

DB7) 8

Pin 15 (LED+)

This pin is used with pin 16(LED-) to setting up the illumination of back light of LCD. This pin is connected with VCC.

9

Pin 16 (LEC-)

This pin is used with pin 15(LED+) to setting up the illumination of back light of LCD. This pin is connected with GND.

ATMEGA 328 The ATmega328 is a single-chip microcontroller created by Atmel in the mega AVR family. The Atmel 8-bit AVR RISC-based microcontroller combines 32 kB ISP flash memory with read-while-write capabilities, 1 kB EEPROM, 2 kB SRAM, 23 general purpose I/O lines, 32 general purpose working s, three flexible timer/counters with compare modes, internal and external interrupts, serial programmable USART, a byte-oriented 2-wire serial interface, SPI serial port, 6-channel 10-bit A/D converter (8-channels in TQFP and QFN/MLF packages), programmable watchdog timer with internal oscillator, and five software selectable power saving modes. The device operates between 1.8-5.5 volts. The device achieves throughput approaching 1 MIPS per MHz.

The Atmel AVR core combines a rich instruction set with 32 general purpose working s. All the 32 s are directly connected to the Arithmetic Logic Unit (ALU), allowing two independent s to be accessed in a single instruction executed in one clock cycle. The resulting architecture is more code efficient while achieving throughputs up to ten times faster than conventional CISC microcontrollers.

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PIN CONFIGURATION

VCC Digital supply voltage GND Ground Port B (PB[7:0]) XTAL1/XTAL2/TOSC1/TOSC2 Port B is an 8-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The Port B output buffers have symmetrical drive characteristics with both high sink and source capability. As inputs, Port B pins that are externally pulled low will source current if the pull-up resistors are activated. The Port B pins are tri-stated when a reset condition becomes active, even if the clock is not running. Depending on the clock selection fuse settings, PB6 can be used as input to the inverting Oscillator amplifier and input to the internal clock operating circuit. Depending on the clock selection fuse settings, PB7 can be used as output from the inverting Oscillator amplifier. If the Internal Calibrated RC Oscillator is used as chip clock source, PB[7:6] is used as TOSC[2:1] input for the Asynchronous Timer/Counter2 if the AS2 bit in ASSR is set. Port C (PC[5:0]) Port C is a 7-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The PC[5:0] output buffers have symmetrical drive characteristics with both high sink and source capability. As inputs,

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Port C pins that are externally pulled low will source current if the pull-up resistors are activated. The Port C pins are tri-stated when a reset condition becomes active, even if the clock is not running. PC6/ RESET If the RSTDISBL Fuse is programmed, PC6 is used as an I/O pin. Note that the electrical characteristics of PC6 differ from those of the other pins of Port C. If the RSTDISBL Fuse is unprogrammed, PC6 is used as a Reset input. A low level on this pin for longer than the minimum pulse length will generate a Reset, even if the clock is not running. Shorter pulses are not guaranteed to generate a Reset. The various special features of Port C are elaborated in the Alternate Functions of Port C section. Port D (PD[7:0]) Port D is an 8-bit bi-directional I/O port with internal pull-up resistors (selected for each bit). The Port D output buffers have symmetrical drive characteristics with both high sink and source capability. As inputs, Port D pins that are externally pulled low will source current if the pull-up resistors are activated. The Port D pins are tri-stated when a reset condition becomes active, even if the clock is not running. AVCC AVCC is the supply voltage pin for the A/D Converter, PC [3:0], and PE [3:2]. It should be externally connected to VCC, even if the ADC is not used. If the ADC is used, it should be connected to VCC through a low- filter. Note that PC [6:4] use digital supply voltage, VCC. AREF AREF is the analog reference pin for the A/D Converter. ADC [7:6] (TQFP and VFQFN Package Only) In the TQFP and VFQFN package, ADC [7:6] serve as anaputs to the A/D converter. These pins are powered from the analog supply and serve as 10-bit ADC channels. Power Supply There is an additional power supply source present in Arduino microcontroller. Power supply port is present at the corner of the Arduino microcontroller. Either we can use this power supply port by connecting with external power supply. (i.e.,ac power supply), or by connecting an dc PT1386619


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power supply through input pins. These power supplies produce an active form to the Arduino microcontroller. These Arduino microcontrollers can accept a range of power supply. When the power supply voltage range Exceeds, the microcontroller gets damaged. Hence, only the particular range of power supply should be given to the Arduino microcontroller. Comparative Analysis of Components Analysis between LCD AND 7 Segment To add a little to Cheaters, reply, one can say that most LCD modules has built in processing power, including a character generator, whereas with the 7 segment displays, whatever you use to control them must supply the processing power to generate the characters to be displayed. From the above you can derive that the LCD modules are able to display more complex characters than the ones you can get with the 7 LED's in a 7-segment display. Some LCD modules will even allow to define own characters or graphics. Analysis between 8051, ARM, PIC and AVR Microcontroller There are some different between 8051, ARM, PIC and AVR Microcontroller 1. 8051, PIC and AVR have Harvard architecture (separate memory spaces for RAM and program memory). ARM has von Neumann architecture (program and RAM in the same space). 2. ARM has a 16 and/or 32-bit architecture. The others are byte (8-bit) architecture. 3. 8051 and PIC have limited stack space - limited to 128 bytes for the 8051, and as little as 8 words or less for PIC. Writing a C compiler for these architectures must have been challenging, and compiler choice is limited. 4. 8051, AVR and ARM can directly address all available RAM. PIC can only directly address 256 bytes and must use bank switching to extend it, though using a C compiler conceals this. 5. 8051 and PIC need multiple clock cycles per instruction. AVR and ARM execute most instructions in a single clock cycle. 6. 8051 and AVR are sufficiently similar that an AVR can usually replace an 8051 in existing products with practically no hardware change. Some AVRs are made with 8051 pinouts to drop right in. The Reset polarity is the main difference. 7. 8051 and AVR instruction sets are different but sufficiently similar that it's possible to translate 8051 assemblers to AVR assembler line by line. Because an 8051 takes 12 (sometimes six) clocks per instruction and an AVR takes only one, one have to modify timing critical routines. PT1386619


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8. AVR and ARM have the best compiler and application , including free GCC compilers. 9. All three Microcontroller’s Feature are Different but AVR Microcontroller’ feature is good compression to other. From the above comparison, it was found that AVR family microcontroller is best suited for the purpose my project as it provides more flexibility than other families. Analysis between Arduino and Raspberry Pi General Purpose While both the Arduino and the Raspberry Pi are very versatile little machines, they both have specific things that they’re good at.

The Arduino, for example, is a microcontroller, which means it excels at controlling small devices like sensors, motors, and lights. This is why the Arduino is best used for projects like building a wake-up light, motion detector alarm, or even a small robot. You’ll also hear people talk about “prototyping” with an Arduino, which is the process of quickly creating a prototype electronic device. If the prototype is successful and the device works, it can be made on a larger scale with printed circuit boards.

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is intended to be used as one. The operating system is rather minimal, so you’ll need some coding knowledge to get the most of it, but that’s one of the things the Raspberry Pi is great at: helping people learn to code. It’s also really good at acting as a server: it can communicate with other computers, serve as an alternative to a Chromecast, provide information, and log data. One redditor puts it perfectly: “My Pi is better at talking to people (running a web server). My Arduino is better at talking to machine parts (moving motors).” Hardware When one looks at an Arduino next to a Raspberry Pi, it’s very clear that the hardware differs quite a bit between the two. Let’s break it down. Power The Arduino’s power supply requirements are very simple; one can plug it into computer or a battery pack, and it will start running code immediately. If the power is disconnected, it will stop; there’s no need to run a shut-down process. The Raspberry Pi, on the other hand, because it has a more full-featured computing system in place, should be shut down like a regular computer, and can be damaged by power cuts. Both the Arduino and the Raspberry Pi have a very low power draw, and can be run for a very long time without using much electricity. Connectivity The Raspberry Pi comes ready to be connected to the internet; it has a built-in ethernet port, and it’s very easy to get a USB Wi-Fi dongle to give it wireless connectivity as well. This is one of the reasons why the Pi is the device of choice for things like personal web servers, printer servers, and VPNs. The Arduino, on the other hand, doesn’t have any built-in capability for connectivity. If one wants to connect it to the internet, it will need to add an extra piece of hardware that includes an ethernet port. If one wants Wi-Fi connectivity, then also it will need a different piece of hardware again. Because the Arduino is meant for hardware projects instead of software ones, it needs a bit of tinkering to get it connected. I/O Pins Input/output pins are what allows single-board computer to talk to things that are connected to it. For example, Raspberry Pi could light up an LED. Or Arduino could activate a motor. If looking for hardware connections, these pins are what one needs. The Raspberry Pi 2 packs 17 of these pins, while the Arduino Uno offers 20. PT1386619


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Another significant difference in the I/O pins between the two boards is the temporal resolution at which can control them. Because the Raspberry Pi is a full computer, it has a number of things that are vying for U time, which means it can have some difficulty in getting timing down to small fractions of a second. And it needs software to properly interface with sensors and other devices. The Arduino, on the other hand, can change the output and monitor the input on its pins down to a very tiny amount of time.

Raspberry Pi Vs. Arduino: Comparison Table Arduino Uno

Raspberry Pi 2 Model B

Cost (base model)

20

39

Processor

16MHz AVR ATmega328P

900 MHz Broadcom ARM CortexA7

Storage

32 KB

n/a

RAM

2 KB

1 GB

I/O pins

20

17

OS

n/a

Raspbian, other varieties of Linux, Android

Languages

Arduino,

Python, C, C++, Java, Ruby

Best for

Hardware / prototyping

Software / server

Power supply

5V USB or DC jack

5V USB

From the above this discussion it can be seen that Arduino is better for projects that has hardware implementation. Since I have to implement my project on hardware so I have selected Arduino. (Albright, 2017)

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CHAPTER 8 DESIGN AND COST ANALYSIS Flow Chart The figure below shows the flow chart of the system. Firstly, face database is prepared for training purpose. Then, the dataset is completed. From these the average face vector of the test image is computed. Average face vector is then subtracted from the original faces for the purpose of calculating the covariance matrix. From the covariance matrix, eigen vectors and eigen values are calculated. Only the K eigen vector corresponding to the K largest eigen values is kept. Feature weight of the training image is calculated. Now the Euclidean distance between the test feature vector and all the training feature vector is computed. Now the test class with minimum Euclidean distance which shows similarity to the test image is found. RFID card is read from the RFID reader. If the image and RFID tag is matched the door opens.

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Block diagram for Data Flow The test image is read from the database and read stored image from the database. Feature extraction using PCA is done from the stored image from the database and test image. The Euclidean distance is measured and if the test image is recognised then the RFID tag from RFID reader is read then the door opens otherwise anyone of the above is not matced the door will not open.

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Block Diagram of Circuit

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CIRCUIT DIAGRAM Power supply As it is well known that the power is the backbone of any electronic system and the power supply is that part which feeds the system. There are various complications in choosing the right supply which can be the critical difference between a device working at optimum levels and one that may deliver inconsistent results. In addition to alternating current (AC) to direct current (DC) power supplies, DC to DC converters are also available. Direct current power supplies are either unregulated or regulated. Regulated supplies come in several options including linear, switched and battery-based. The Circuit diagram for the power supply is mentioned below in the figure which is required for the software implementation of power supply block. A bridge rectifier is used to rectify the input and it also checks the polarity of the input supply.

Figure 13: - Power supply

In this circuit diagram, it can be clearly seen that the 12V ac input is fed to the bridge rectifier which gives 12V pulsating DC. This DC voltage is filtered through the capacitor to remove the ripples. The clarified DC is fed to 7805 regulators to fetch +5v regulated output voltage. This regulated voltage is given to all the components to function properly. LCD INTERFACING LCD which stands for Liquid Crystal Display is an electronic display module and find a wide range of applications. A 16x2 LCD display is very basic module and is very commonly used in various devices and circuits. These modules are preferred over seven segments and other multi segment LEDs. The reasons being behind this is that the LCDs are economical, easily programmable and have no limitation of displaying special and even custom characters unlike in seven segments, animations and so on.

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A 16x2 LCD means it can display 16 characters per line and there are 2 such lines. In this LCD each character is displayed in 5x7 pixel matrix. This LCD has two s naming Command and Data. The command stores the command instructions given to the LCD. A command is an instruction given to LCD to do a predefined task like initializing it, clearing its screen, setting the cursor position, controlling display etc. The data stores the data to be displayed on the LCD. The data is the ASCII value of the character to be displayed on the LCD. In this segment of the project the interfacing of the LCD with the microcontroller has been discussed. For LCD Port D is defined as the output port. Among all the 16 pins the pins Vss, Vdd and Vee is not requiring connecting in proteus. Because it’s automatically activated. The pins Rs, RW and E are connected with the port PD0, PD2 and PD3 respectively. The pins from D4 to D7 are connected at port PD4 to PD7 respectively. It displays the simple output provided by the microcontroller.

Figure 14: - LCD interfacing

RESET SWITCH For reset there is also a predefined pin that is PC6.The switch is connected through resistor and capacitor. This is done because of the security persistence of the switch which allows ing a specific current through switch.

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Figure 15: - Reset switch

Block Diagram for the main functions to be called in MATLAB GUI

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Block Diagram for Creating the Database for Faces by Calling Functions

Block Diagram for Adding an Image in Database

Block Diagram for the Options in Capture Now

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Block Diagram for the Option of Deleting an Image and Folder from Database

Block Diagram for the Option of Face Recognition

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Cost Estimation Accurate cost estimates help developer to complete a project on-budget. During the conceptual phase when project selection occurs, economic factors are an important consideration when choosing between competing projects. Before starting to perform cost estimation, researcher need to have completed the following processes define Aim, objective, scope, proper planning, and project management, define activities, Sequence activities, estimate activity resources, and estimate project Duration. Project underestimation of resources and costs is one of the most common contributors to project failure. As such, developer should be knowledgeable about the tools, components, required to execute the project so that researcher can easily do the cost estimation. Developer should estimate costs for all resources that will be charged to the project. As per customer view, if the cost of the product is economic and good response then its demand will be high. So, calculation is required to evaluate the price of product and the cost of all the components required to design the product. Here developer is deg the servo voltage stabilizer so developer keep some note in mind by investing minimum amount the maximum

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output should be obtained. Here costs of tools are not mentioned to mount the circuit on printed circuit board like Soldering iron, wire etc. Cost Estimation for the Prototype Cost of list of components MATLAB Software Arduino Software ATMEGA328 microcontroller RFID reader Type B Standard 2.0 USB cable Connectors Common Voltage Hub Servo motor as door lock Total Cost of the Prototype

Free Free Rs130 Rs 399 Rs 500 Rs 100 Self-made Rs 300 Rs 2578

Costs for the Industry The industrial cost comprises of the installation cost and the components cost for the company to manufacture the product. Industrial cost also includes the maintenance cost of the setup. The product will be sold to the company including the component ‘s cost and cost of developer’s efforts. The costing of the product to the company is as follows:

S. No.

MODULES FOR COSTING COST

1

Cost of the product to the

Rs. 7400.00

developer 2

Labor cost per day

Rs. 200

3

Number of days of work

90

(days) 4

Effort cost of the developer

Rs. 3,000.00

5

Set up cost

Rs. 20,000.00

Cost paid by the end The cost of the will include the cost of product after adding the cost of the company and all of the taxes. The cost of the finished product in the market is designed as follows: S. No. Taxes to be Included

COST (in Rs)

in the costing 1

Single product cost

Rs. 6000

bid by the industry

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2

Sale Tax on the

Rs. 769.8

Product (10.5%) 3

TOTAL COST

Rs. 8260

Conclusion for cost estimation From the above results, it was found that the cost of developing the prototype by the researcher is a little more than 2500 rupees. While the same for developing on industrial level is 20000 rupees and when it is marketed or given to an end the industry has placed a bid of a little over 8000 rupees which is not very costly thus increasing the area of the target audience of the system.

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CHAPTER 9 SYSTEM IMPLEMENTATION Software Implementation The software used in the project for implementation are MATLAB and Arduino. MATLAB ‘MATLAB’ which stands for Matrix Laboratory, is basically a very advanced software which is used for the technical computing. It is a high-performance language which is used for the mathematical and computational operations along with the algorithm development, modelling, simulation, prototyping, data analysis, and visualization and for many other scientific graphics generation. The software provides the optimized platform for solving the engineering and scientific problems. This software also consists the built-in graphics which makes the concepts easily understandable and gaining the insight knowledge of the concept also gets very easy. There are various uses of the MATLAB software but the typical uses are as follows: 1. Math and computation 2. Algorithm development 3. Modeling, simulation, and prototyping 4. Data analysis, exploration, and visualization 5. Scientific and engineering graphics 6. Application development, including Graphical Interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar no interactive language such as C or FORTRAN. MATLAB was originally written PT1386619


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to provide easy access to matrix software developed by the LINPACK and EISPACK projects, which together represent the state-of-the-art in software for matrix computation. MATLAB has evolved over a period of years with input from many s. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis. MATLAB features a family of application-specific solutions called toolboxes. Very important to most s of MATLAB, toolboxes allow to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others. MATLAB is the high-level language and interactive environment used by millions of engineers and scientists worldwide. It lets developer to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance. Developer can use MATLAB in projects such as modeling energy consumption to build smart power grids, developing control algorithms for hypersonic vehicles, analyzing weather data to visualize the track and intensity of hurricanes, and running millions of simulations to pinpoint optimal dosing for antibiotics. (Nasre, 2011) Coming to the MATLAB system, it consists of five main parts which are as follows: The MATLAB language. This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick and dirty throw-away programs, and "programming in the large" to create complete large and complex application programs. The MATLAB working environment. This is the set of tools and facilities that allows to work with as the MATLAB or programmer. It includes facilities for managing the variables in the workspace and importing and exporting data. It also includes tools for developing, managing, debugging, and profiling M-files, MATLAB's applications. Handling Graphics: This is the MATLAB graphics system. It includes high-level commands for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation PT1386619


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graphics. It also includes low-level commands that allow you to fully customize the appearance of graphics as well as to build complete Graphical Interfaces on your MATLAB applications. The MATLAB mathematical function library. This is a vast collection of computational algorithms ranging from elementary functions like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast Fourier transforms. The MATLAB Application Program Interface (API). This is a library that allows to write C and FORTRAN programs that interact with MATLAB. It includes facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a computational engine, and for reading and writing MAT-files. (Jordan, 2017) Proteus for Circuit Deg Proteus professional is a software combination of ISIS schematic capture program and ARES PCB layout program. This is a powerful and integrated development environment. Tools in this suit are very easy to use and these tools are very useful in education and professional PCB deg. As professional PCB deg software with integrated space based auto router, it provides features such as fully featured schematic capture, highly configurable design rules, interactive SPICE circuit simulator, extensive for power planes, industry standard CADCAM & ODB++ output and integrated 3D viewer. Features 1. As fully featured schematic capture 2. Highly configurable design rules 3. Interactive SPICE circuit simulator 4. Extensive for power planes 5. Industry standard CADCAM & ODB++ output 6. Integrated 3D viewer. MICROSOFT PROJECT Microsoft Office Project is help full for generate Gantt chart. It is also referred to as Microsoft Project, is a suite of tools for more efficient project and portfolio management. Project is used in a variety of industries including construction, manufacturing, pharmaceuticals, government, retail, financial services and health care.

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The main modules of Microsoft Project include project work and project teams, schedules and finances. Microsoft Project is designed to help s set realistic goals for project teams and customers by creating schedules, distributing resources and managing budgets. The Project Guide helps s create projects, track tasks, and report results. The software helps contractors gain control over their resources and finances by simplifying the assignment of resources to tasks and budgets to projects. Microsoft Project also comes with a customizable wizard that walks s through the process of project creation, from asg their tasks and resources to reporting the final results. Microsoft Project management software is closely integrated with Microsoft Office suite and also includes a Client Access License (CAL) that allows easy connection with Office Project Server. MINITAB: -This software is helpful for Analysis the questionnaire. Software Implementation On MATLAB For the implementation of the project on software the developer has used MATLAB for the programming purpose. In this firstly basic function were created which were necessary from the context of project. Then these functions were called into a main program which then formed the GUI of the system. The function with their screenshot are listed below. 1. The first step was initialization of webcam for which the developer has created a function named inicamera.m. Later in the system this function will be called upon wherever needed. Here the webcam would start to record a video.

2. The second step was to capture an image which would be used as test image, database and training the system. The developer has named the function as capture.m. Here, the snapshot of the image would be taken.

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3. The third step would be to save the image in the desired form as that required by the system. For this the developer has declared saveimage.m as a function. This function will also be called wherever desired and would be saved on the required path.

4. Now for initializing all the above function at one place another function named capturenow.m has been declared were the above steps are executed at once.

5. For the fifth step, the need of the system is to create a database. In database, the above function has been called under the choice of add an image. For this the developer declared another function by the of createdatabase.m. This function has other choices also that include add a folder that would directly lead to the database folder of the system.

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6. In the above step the developer gave an option of creating the database so there has to be an option of deleting the database. For a function named delete database has been declared. Here, the option of deleting an image as well as folder has been given. The name of the function is deletedatabase.m.

7. In this step the training of database has been implement for the training purpose. Here the face are aligned and represented in the form 2D image and a matrix is constructed. Further the 2D image is converted into 1D image.

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8. Now for the purpose of implementing the Eigen face apporach another program is created. Where the mathematics of PCA is implemented. The file goes by the name of eigenface.m core.

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9. Now a recognition function for the purpose of giving real time results has been programmed, the file is named as recognition.m.

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10. Now for main comparsion of the images saved in database, the average face and the capture that had to be compared a function file named facerec.m is made. This file will be always be opened while running the system. This also where the interfacing of hardware is done. Here, a short code for the serial communication of data with hardware is written.

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11. Lastly the file for the GUI is programmed were a menu is created. This file is the main file where all the above function are being called at once and the developer can navigate to different fuction simply by running this file.

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12. Every part of the GUI has also been provided by an exit button also. This to facilitate the transaction from one menu to the other.

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Graphical Interface of the System 1. When system is executed the following menu will appear. This is main menu where options of creating the database, deleting the database, system training, face regognition and exit are given.

2. When the option of creating database is clicked the following menu appears. Here the options of adding an image, adding a folder and exit are available.

3. In creating the database, when add an image option is selected then a new menu pops up in which further more options are available. In this menu, the options available are choose from file, capture now and exit are available.

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4. When the option of choose from file is choosen then it directs to system where one can navigate choose the file as desired.

5. On choosing the option of capture now, the below given menu appears where there are options of intializing the camera, capturing and saving the image is availabe.

6. Now when existing from the create database menu, the second option in the main menu is of deleting the database where options of delete an image, delete a folder and exit are available.

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7. After going through the option of delete database comes the option of train system in the main menu. Here the image to trained is selected. In the figure, there are two folders of test image and train database is given. For the purpose of training the train database folder is selected.

8. Again when redirected to the main menu with help of exit button. The next option is of face recognition where a new menu of input image from file i.e. image of the person whose face has to be recognised can be choosen or a new image for the purpose of recognition can be captured. When going for the capture now option the same menu for capture now that was shown while explaining the database comes up. Last button of the face recognition menu is recognition which used for checking the test image and equivalent image. If the test image is of the same person as that of equivalent image generated by the system then the system has recognised the face.

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Hardware Implementation After performing all the procedures before the implementation of the hardware it is required to have a brief overview about how the hardware is going to be implemented for the successful implementation of the circuit. The every segment of the system needs to be calibrated individually and properly for the working of the hardware circuit in the desired manner. There is lots of difference between hardware and software implementation. In simulation power supply are not required to connect because it is predefined. It’s required to define the output and inputs port according to the connection of the circuit as drawn in software implementation part. In the case of this system implementation the power supply is provided through the USB port of the computer. The whole implementation of the hardware has been divided into certain sub categories for the better understanding of the implementation.

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LCD Interfacing In this segment of the hardware simulation it can be clearly seen that the LCD is calibrated and connected with the Arduino UNO. In this the different ports of the LCD which is connected to the Arduino UNO is such as pin 7, pin 8 , pin 9, pin 10, pin 11 and pin 12 of the Arduino UNO is connected with the RS, E, D4, D5, D6, and D7 pins of the LCD respectively. The remaining pins of the LCD naming K, A, RW, V0, Vdd, and Vss is connected to the 5V power supply, GND, GND of voltage hub, preset of Voltage-hub, 5V (Voltage hub) and GND of the voltage hub respectively. The interfacing of the LCD and Arduino UNO has also been shown in the figure mentioned above the paragraph but due to the large number of wires connected it is hard to differentiate that which wire is connected to which pin of the micro-controller by only watching from the picture. So it is required to have a clear look upon the connection diagram in the software implementation attentively.

RFID Module Interfacing For the interfacing of RFID, there are three pins in the module which are GND, Tx and +5V. Now the two of the three pins are connected to voltage hub, out of these two pins one is ground and other is +5V. The ground pin of the module is connected to the ground of the voltage hub while the +5V is connected to the +5V of the voltage hub. Tx pin of the module is connected to the microcontroller digital pin no. 5.

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Servo Motor There three connection in servo motor. Two of them are connected to the voltage hub, one is connected to 5V of the voltage hub and other to the ground of the voltage hub. The third pin is connected microcontroller pin Ao.

Final Working Hardware

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