Apply Multi-Biometrics Recognition Systems for Intelligent House Management

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1 Hong Kong Institute of Vocational Education (Tsing Yi) Department of Multimedia and Internet Technology Higher Diploma in Multimedia and lnternet Applications (41381) Final Year Project Report Apply Multi-Biometrics Recognition Systems for Intelligent House Management By Chan Ka Lun, Peae Tsang Man Piu Academic Year 2005/06 Supervised by Mr. MK Lam

2 ABSTRACT This final year project describes the design and development of applying the biometrics technology with facial recognition and fingerprint recognition system. Several commonly used applications for our biometric system are reviewed in this report. Details of the design criteria of both hardware and software principles of operation as well as the testing and verification of the system are presented. Further development of project is also proposed and recommended. This project is to develop and built up a biometrics system for intelligent house and clubhouse. As biometrics is a popular and safety technology, the project applies this technology to improve the security level and easy for management. i

3 TABLE OF CONTENTS page ABSTRACT TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES LIST OF APPENDICES i ii v viii ix Chapter 1 Introduction Overview Background Information Structure of the Final Year Project Report 1 Chapter 2 Literature Review Introduction Introduction to Biometrics Technologies What is Biometrics? History of Biometrics How does it work? Applications in Biometrics Template size of each Biometrics Technology Examples of biometrics system in my life Examples of biometrics system layout Examples of hardware design Examples of software interface design Summary 12 Chapter 3 Development Methodology for Multi-Biometrics Recognition System Introduction Design Consideration of Hardware System Design Consideration of Software Graphics Design and Layout Structure of Database Design 19 ii

4 Chapter 4 Details of the Multi-Biometrics Recognition System Overview Workflow of Recognition System Function of Recognition System Function of Administration tools Function of Building security system Function of Clubhouse system Hardware of Recognition System Development Software use Software needs and setup Database Structure Relational between tables Householder Householder Log Visitor Building Floor and Flat Clubhouse Graphics Design and Layout Administration Tools layout design Building security system layout design Clubhouse system layout design 35 Chapter 5 Evaluation of Multi-Biometrics Recognition Systems Overview Principle of Biometric Enrolment and Verification Summary 38 Chapter 6 Application Procedure of Multi-Biometrics Recognition Systems Administration tools Add new householder Edit householder Delete householder Householder entry log Visitor entry log Building Security System 49 iii

5 6.2.1 Householder mode Visitor mode Clubhouse system Clubhouse login and logout system Clubhouse payroll system 57 Chapter 7 Verification of the Multi-Biometrics Recognition Systems Overview Verification of Result Summary 59 Chapter 8 Conclusion and Further Development Overview Contribution of Final Year Project Discussion on the Project Limitation Further Proposal 61 References 62 Appendices 64 Appendix A: Team member workload 64 Appendix B: Project plan 65 Appendix C: Programming code 66 iv

6 LIST OF FIGURES page Fig. 2.1 Biometric identification and encrypted data system 8 Fig. 2.2 Biometric access control system (Exys Security system) 8 Fig. 2.3 Country entry and exit control system 8 Fig. 2.4 Hand geometry control system 9 Fig. 2.5 Biometric experiment system 9 Fig. 2.6 Presse Fotos ekey fingerprint system 10 Fig. 2.7 Biometric reader clocking system 10 Fig. 2.8 Office access control system 10 Fig. 2.9 Windows lcqon recognition system 11 Fig Breaker facial searching system 11 Fig Biometric Patient Tracking System 11 Fig fingerid system with biometrics in USA 11 Fig. 3.1 Types of fingerprint 13 Fig. 3.2 Optical Fingerprint Sensors 14 Fig. 3.3 Surface Pressure Sensor 14 Fig. 3.4 Capacitive Sensors 14 Fig. 3.5 Facial geometry example 15 Fig. 3.6 Correct recognition example 15 Fig. 3.7 Facial recognition in airport 16 Fig. 3.8 First Visitor mode layout 17 Fig. 3.9 Second Visitor mode layout 18 Fig Clubhouse mode layout 18 Fig Database schema of Biometrics System 19 Fig. 4.1 Structure of System 20 Fig. 4.2 Workflow of Building security system 21 Fig. 4.3 Workflow of Clubhouse system 22 Fig. 4.4 Marcomedia Flash 25 Fig. 4.5 Adobe Photoshop 25 Fig. 4.6 Visual Basic Fig. 4.7 Create MySQL user 26 Fig. 4.8 Restore the backup file 27 Fig. 4.9 Permission of the folder 27 Fig Folder of householder and visitor 28 Fig Database Structure of Systems 28 v

7 Fig Main page of Administration Tools 32 Fig Database Management of Householder 32 Fig Entry Log of Householder 33 Fig Entry Log of Visitor 33 Fig Selection mode of building security system 34 Fig Householder mode of building security system 34 Fig Visitor mode of building security system 35 Fig Language Selection mode of clubhouse system 35 Fig Selection mode of clubhouse system 36 Fig Login mode of clubhouse system 36 Fig Choose facilities mode of clubhouse system 37 Fig Logout mode of clubhouse system 37 Fig. 6.1 Select Database Management 39 Fig. 6.2 Click the Add New button 39 Fig. 6.3 Add user windows 40 Fig. 6.4 Procedure of face enrollment 40 Fig. 6.5 Successful message of face enrollment 41 Fig. 6.6 Successful message of add new householder 41 Fig. 6.7 Error message of add new householder 42 Fig. 6.8 New householder in the list 42 Fig. 6.9 Click the Edit button 43 Fig Enroll new face 43 Fig Successful message of face enrollment 44 Fig Successful message of update householder 44 Fig Delete the householder 45 Fig Confirm to delete householder 45 Fig Select Entry Log of householder 46 Fig Query of Householder Entry Log 46 Fig Result of Householder Entry Log 47 Fig Select Entry Log of visitor 47 Fig Query of Visitor Entry Log 48 Fig Show the photo of visitor 48 Fig Mode selection of Building Security System 49 Fig Procedure of identify the householder 49 Fig Error message when cannot identify the householder 50 Fig Householder identify complete 50 Fig Mode selection of Building Security System 51 Fig Procedure of enter the floor and flat 51 vi

8 Fig Error message of enter wrong floor 52 Fig Procedure of face capture 52 Fig Procedure of fingerprint capture 53 Fig Visitor register complete 53 Fig Step 1 of process clubhouse system 54 Fig Step 2 of process clubhouse system 54 Fig Step 3 of process clubhouse system 55 Fig Step 4 of process clubhouse system 55 Fig Step 5 of process clubhouse system 56 Fig Step 6 of process clubhouse system 56 Fig Step 1 of process clubhouse payroll system 57 Fig Step 2 of process of clubhouse payroll system 57 Fig Step 3 of process of clubhouse payroll system 58 vii

9 LIST OF TABLES page Table 2.1 Template size of each Biometrics Technology 7 Table 4.1 Database structure of householder 29 Table 4.2 Database structure of householder log 29 Table 4.3 Database structure of visitor 30 Table 4.4 Database structure of building 30 Table 4.5 Database structure of floor and flat 31 Table 4.6 Database structure of clubhouse 31 Table 7.1 FRR rate of facial recognition 59 Table 7.2 FAR rate of facial recognition 59 viii

10 LIST OF APPENDICES page Appendix A Team member workload 64 Appendix B Project plan 65 Appendix C Programming code 66 ix

11 Chapter 1 Introduction 1.1 Overview Nowadays, Biometrics becomes important in our life. Since it has a high security and convenient, so that people use Biometrics technology on anything. Such as, door access control, border control, banking, identify people, intelligent house, etc. The application is very diverse and their capacity of development is very large. The intelligent house becomes popular for the past few years. It can provide a safety and comfortable to the householder. But the security is very important, it must be identify the house holder accurately and prevent the system to make a mistake for identification. So that, we need to build a system like the security to make a identity and monitoring the security of building. 1.2 Background Information In fact, Hong Kong building security is decreased. The thief is increased to make the people safety to be damaged. So, we must improve the building security and the management. By the way, when we come back to our building, we may use words of password or the key to open the building door. That is so trouble and inconvenience. Biometrics technologies can help us to improve the security and easy to management. So, we may use the biometrics technologies to maintain our final year project. 1.3 Structure of the Final Year Project Report The remainder of this report is divided into Seven Chapters. They are as follows: - Chapter 2 covers the literature review on the design research of biometrics system. We find a lot of the information and image from Internet. Chapter 3 discusses on the design criteria of hardware system, software, layout and database Chapter 4 presents the details of the components of the hardware software, workflow and database about our system. Chapter 5 describes the performance evaluation of the developed system about the principle of biometric enrolment and verification. Chapter 6 and 7 describe the enrolment and verification procedure of how to capture and record of our recognition system. 1

12 Chapter 8 concludes the report and out lines the contributions of the project. The limitations were highlighted and suggestions are made for further development to improve the system. 2

13 Chapter 2 Literature Review 2.1 Introduction That part is literature review. It means we will show you all our research before our project starting. And the research should be about example of biometric system, layout of the hardware system, layout of the software interface, etc. We will show you all of our research for the final year project. 2.2 Introduction to Biometrics Technologies What is Biometrics? The term biometrics refers to the emerging field of technology devoted to the identification of individuals using biological traits or behaviours [1]. In practice, this means capturing an image of a unique feature of an individual such as a fingerprint, hand, eye or face, and comparing it with a template captured previously. For ease of explanation this has been over-simplified, but in essence this is biometric technology works. Human Recognition Systems works with 4 key biometric technologies: Iris Recognition Hand Recognition Fingerprint Recognition Facial Recognition History of Biometrics 1800s identification largely relied upon "photographic memory" [2]. This was changed in 1883 when Alphonse Bertillon, a clerk in the Paris police prefecture, introduced physical measurement system (which came to bear his name: bertillonage) Bertillon based his system on the claim that measurement of adult bones does not change throughout life. Measurements in his system included: height, length, and breadth of the head, the length of different fingers, the length of forearms, etc (Bertillon estimated that the odds of duplicate records were 286,435,456 to 1 if 14 traits were used. After this, the police used finger printing, which was developed by Richard Edward Henry of Scotland Yard, instead. Essentially reverting to the same methods used by 3

14 the Chinese for years. However the idea of biometrics as a field of study with useful identification applications, was there and interest in it has grown. Personal Identification Numbers (PINs) were one of the first methods used for identification. There are also methods that involve passwords and physical tokens e.g. smart cards. There are a number of problems associated with this kind of identification. People forget passwords, lose tokens, reuse passwords, write them down, tokens can get stolen. The recognition of the above does not mean the identification of the person providing it - they could be presented by anybody. With the advent of e-commerce brings increased pressure on IT managers to provide partners and customers with means of identification to access corporate networks. This results in a deluge of passwords and means of access, which are often forgotten, misused or lost. With the increased population accessing these networks the risk of attacks on the networks is increased. Companies are turning to Biometric Systems to bring tighter security to their computer networks. Today we have the technology to realize the aims, and to refine the accuracy of biometric identification, and therefore the possibility of making it a viable field. Soon biogenetics technology will be used in every house hold and it is getting increasingly more and safer to use biometric technologies in order to better our life How does it work? False Reject Rates: For most applications, letting the good guys in is just as important as keeping the bad guys out [3]. The probability that a biometric device won't recognize a good guy is called the "False Reject Rate." The False Reject Rates quoted for current biometric systems range from % to 1.0%. A low False Reject Rate is very important for most applications, since users will become extremely frustrated if they're denied access by a device that has previously recognized them. 4

15 There has a example may be helpful. A company with 100 employees has a biometric device at its front door. Each employee uses the door four times a day, yielding 400 transactions per day. A False Reject Rate of 1.0% predicts that every day, four good guys (1% of 400) will be denied access. Over a five-day week, that means 20 problems. Reducing the False Reject Rate to 0.1% results in just two problems per week. A low False Reject Rate is very important for most applications, since users will become extremely frustrated if they're denied access by a device that has previously recognized them. As mentioned previously, the combination of a low False Reject Rate plus a simple keypad code provides virtually unbreakable security. Equal Error Rates: Error curves give a graphical representation of a biometric device's "personality." The point where false accept and false reject curves cross is called the "Equal Error Rate." The Equal Error Rate provides a good indicator of the unit's performance. The smaller the Equal Error Rate, the better. Validity of Test Data: Testing biometrics is difficult, because of the extremely low error rates involved. To attain any confidence in the statistical results, thousands of transactions must be examined. Some error rates cited by manufacturers are based on theoretical calculations. Other rates are obtained from actual field testing. Field data are usually more reliable. In the case of False Reject rates, only field test data can be considered accurate, since biometric devices require human interaction. For example, if the device is hard to use, false reject rates will tend to rise. A change in the user's biometric profile could also cause a false reject (a finger is missing, for example). None of these conditions can be accurately quantified by purely theoretical calculations. On the other hand, False Accept Rates can be calculated with reasonable accuracy from cross-comparison of templates in large template databases. 5

16 Currently, most field test error rates have been generated by various biometric manufacturers using end-user data. Tests have also been conducted by independent laboratories such as the U.S. Department of Energy's Sandia National Laboratories. The most recent test released by Sandia was performed in It's important to remember that error rates are statistical: they are derived from a series of transactions by a population of users. In general, the larger the population and the greater the number of transactions, the greater the confidence level in the accuracy of the results. If the error rate is reported at 1:100,000, and only 100 transactions were included in the study, the confidence level in the results should be very low. If the same error rate was reported for 1 million transactions, the confidence level would be much higher. The magnitude of the reported results affects the size of the sample required for a reasonable confidence level. If the reported error rate is 1:10, then a sample of 100 transactions may provide a sufficient confidence level. Conversely, a 100-transaction sample would be too small if the error rate was reported as 1:100, Applications in Biometrics There are 9 biometrics applications: [4] Physical Access Control Logical Access Control Justice/Law Enforcement Time and Attendance Border Control/Airports HIPAA Financial/Transactional Integrators/Resellers Safes, Door Locks 6

17 2.2.5 Template size of each Biometrics Technology Biometrics Technology Iris Fingerprint Facial Hand Geometry Template size per record 256 Bytes Bytes >512 Bytes 9 Bytes Table 2.1 Template size of each Biometrics Technology 2.3 Examples of biometrics system in my life Law Enforcement The law enforcement community is perhaps the largest biometrics user group. Police forces thought the world use AFIS technology to process criminal suspect, match finger images and bring guilty criminals to justices. A number of biometrics vendors are earning significant revenues in this area. Banking Bank has been evaluating a range of biometrics technologies for many years. Automated Teller Machines (ATMs) and transaction at the point of scale are particularly vulnerable to fraud and can be secured by biometrics. Other emerging markets such as telephone banking and internet bank must also be totally secure for bankers alike. 7

18 2.4 Examples of biometrics system layout We researched that the following figures are the examples of the biometrics system. Fig. 2.1 Biometric identification and encrypted data system [5] Fig. 2.2 Biometric access control system (Exys Security system) [6] Fig. 2.3 Country entry and exit control system [7] 8

19 Fig. 2.4 Hand geometry control system [8] Fig. 2.5 Biometric experiment system [9] 9

20 2.5 Examples of hardware design We may show you our research of hardware. The following figures are below. Fig. 2.6 Presse Fotos ekey fingerprint system [10] Fig. 2.7 Biometric reader clocking system [11] Fig. 2.8 Office access control system [12] 10

21 2.6 Examples of software interface design The following figures are our research of the biometrics software interface examples. Fig. 2.9 Windows lcqon recognition system [13] Fig Breaker facial searching system [14] Fig Biometric Patient Tracking System [15] Fig fingerid system with biometrics in USA [16] 11

22 2.7 Summary After the research that, we find many about the information of biometrics and many different kinds of the biometrics system. In fact, there were many technologies were used the biometrics technology. It is the truth and it must be more popular in the future. Now a day, many old systems are displaced by the biometric system. For example: access control system, payroll system, banking system, etc. During the project development, the biometric hardware is used. There are TiFace system, U are U fingerprint system. TiFace system enables to capture and enrol user s face. U are U fingerprint system is use to capture and enrol user s fingerprint. So, we have to use the biometric in the future life. 12

23 Chapter 3 Development Methodology for Multi-Biometrics Recognition System 3.1 Introduction In this part, we will tell you about our first development methodology. What kinds of hardware did we use. Why we use that hardware. What kinds of software did we use. Why we use that software. And we also post our first layout design in that part. We will all tell you the answer 3.2 Design Consideration of Hardware System Fingerprint recognition represents the oldest method of biometric identification. Its history is going back as far as at least 2200 BC. The use of fingerprints as a personal code has a long tradition and was already used by the Assyrians, the Babylonians, the Chinese and the Japanese. Since 1897, dactyloscopy (synonym for non-computer-based fingerprint identification) has been used for criminal identification. A fingerprint consists of ridges (lines across fingerprints) and valleys (spaces between ridges). The pattern of the ridges and valleys is unique for each individual. [17] There are two major methods of fingerprint matching: Minutiae matching and global pattern matching. The first approach analyses ridge bifurcations and endings, the second method represents a more macroscopic approach. The last approach considers the flow of ridges in terms of, for example, arches, loops and whorls. As the equal-error-rate is low, therefore fingerprint recognition is very accurate. The prices of such systems compared to other biometric systems are quite low and the user acceptance is very high. The strength of fingerprint identification is, that it can be deployed in a varied range of environments. Fig. 3.1 Types of fingerprint [18] 13

24 There are 6 types of sensors for fingerprint recognition: Optical Fingerprint Sensors Themalelectric Sensors Capacitive Sensors E-Field Sensors Touchless Sensors Surface Pressure Sensor Fig. 3.2 Optical Fingerprint Sensors [19] Fig. 3.3 Surface Pressure Sensor [20] Fig. 3.4 Capacitive Sensors [21] The template size varies from 100 bytes to 1500 Bytes depending on the algorithm and the quality of a fingerprint. Nevertheless, very rarely there are fingerprints without any minutiae-points that leads to a failure to enroll (FER = Failure to Enroll Rate). It is also difficult to extract the minutiae points accurately when the fingerprint has got a low quality. 14

25 Introduction of facial recognition Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a "template." Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver's license databases. [22] Computers can do increasingly amazing things, but they are not magic. If human beings often can't identify the subject of a photograph, why should computers be able to do it any more reliably? The human brain is highly adapted for recognizing faces - infants, for example, remember faces better than other patterns, and prefer to look at them over other patterns. The human brain is also far better than computers at compensating for changes in lighting and angle. The fact is that faces are highly complex patterns that often differ in only subtle ways, and that it can be impossible for man or machine to match images when there are differences in lighting, camera, or camera angle, let alone changes in the appearance of the face itself. Fig. 3.5 Facial geometry example [23] Fig. 3.6 Correct recognition example [24] 15

26 All of this makes face recognition ideal for high traffic areas open to the general public, such as: Airports and railway stations Casinos Cashpoints Stadiums Public transportation Financial institutions Government offices Businesses of all kinds Fig. 3.7 Facial recognition in airport [25] 16

27 3.3 Design Consideration of Software We choose macromedia flash and adobe Photoshop for our design software. Why we use that two software for design development? It is because macromedia flash is so powerful. It can be used actionscript for many different of animation. Although, it is easy to use and popular in the design area. The second point is we don t want to have a static and boring interface. So, we decided to use flash animation to replace of static picture interface. It can attract the people to use our system and make the system more interest. In the internet, many people used that two software. It is the brand name power and that software is really good for design. On the other hand, Photoshop can help us for tuning the photo color, light, size, effect, etc. It also can help us to make the picture be good. 3.4 Graphics Design and Layout Fig. 3.8 First Visitor mode layout 17

28 Fig. 3.9 Second Visitor mode layout. Fig Clubhouse mode layout 18

29 3.5 Structure of Database Design The following Fig is our first development of database structure design. It only consists of 4 tables: tblhouseholder, tblbuilding, tblvisitoryymmdd and tblfloorflat. That is our first developed schema. Fig Database schema of Biometrics System 19

30 Chapter 4 Details of the Multi-Biometrics Recognition System 4.1 Overview In this part, we finished to discuss and develop all the kinds of tool should we use. We will use two hardware for the system. It should be Face recognition and fingerprint hardware. And we will tell you the function of our biometric system. It has three different parts of the recognition system, please refer to Fig Finally, we choose the software, hardware and the final layout. We will show you all the things in the following. Fig. 4.1 Structure of System 20

31 4.2 Workflow of Recognition System The following two figures 4.2 & 4.3 are our workflow of the building security system and clubhouse system. Entry Select Mode Visitor Input Floor and Flat Householder Facial Recognition Enroll Face Invaild Check database & identify Enroll Fingerprint Vaild Open door Open door Fig. 4.2 Workflow of Building security system 21

32 Entry Select Login/Logout Logout Login Login mode Logout mode Facial recognize Facial recognize Invaild vaild Invaild vaild Check database Check database Store login time to database Store logout time to database Fig. 4.3 Workflow of Clubhouse system 4.3 Function of Recognition System There are three parts of our recognition system. It consists of Administration Tools, Building security system and Clubhouse system Function of Administration Tools Actually, householder and visitor also need to enroll fast when they want to use the biometric system. We make an enrolment to analysis this problem. Householder can come to our office to use the householder enrolment system. They only need to put their face in front of the camera. Their face record will store to our database for verify used. They also need to type their information (e.g. name, 22

33 gender, and floor no., flat no., etc.) to our database used. It is used for comparison Function of Building security system We design that householder mode is for householder using. They are no need to remember the word of password for house entry. It is because the entry password always changed a time. The householder may forget the password or remember the wrong one. So, Householder mode system can help person easily for entry. They only put their face in front of camera. If they are our householders then the door will open automatically by our system. Nowadays, people visit to their friend house. They must write down their personal information (e.g. name, id, gender, visit reason, visit room no.) in the building entry control. It is because the building needs to protect their householder safety and giving record. It may make the visitor inconvenient. So, we make a visitor mode for visitor using. It can help visitor more convenient, Visitor only put their face in front of camera and fingerprint to record their personal data. That should be fast and more convenient than the paper record Function of Clubhouse system Clubhouse is to be more popular to the new building. Householders can use their own clubhouse facilities. When they used the clubhouse facilities, they need to make a paper rent. That is not convenient and trouble. So, we make a clubhouse biometric system for householder using. They only open our system and put their face in front of the camera. Our system will record their login time. Otherwise, when they want to logout and leave that they also use our system to record their logout time. They can use our clubhouse easily. When you finished your playing from clubhouse, you need to pay the free to the clubhouse using. We make a payroll system for clubhouse payment. Householder only put their face in front of camera. Our database will search your clubhouse login time and logout time used for calculate the price. When you know the price that you only pay the money to our staff. He will help you to finish the payment process. 23

34 4.4 Hardware of Recognition System We choose facial recognition and fingerprint for our system used Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. These systems depend on a recognition algorithm. The first step for a facial recognition system is to recognize a human face and extract it fro the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features. Facial recognition is a good biometric system and it is too convenience and user-friendly. So that, we use facial recognition for our system used. For the fingerprint reason, we found that following good points: More securable than passwords by intercepting use and approach of Unauthorized people whose identification is not registered. Free from inconvenience to upgrade and remember passwords and the company It can save much cost to manage passwords. Fast and exact perception with high technology of finger print identification. Adopted dsp system that is to calculate moving direction and distance without Password by the image perceiving sensor. No roller and soft movement thanks to less rubbing resistance. Finally, fingerprint is easy too use, user-friendly and too cheap in the kinds of biometrics devices. 24

May 2010. For other information please contact:

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