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Design and Implementation of a Vascular Pattern Recognition System

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Design and Implementation of a Vascular Pattern Recognition System Design and Implementation of a Vascular Pattern Recognition System A Thesis submitted to the Graduate School Of The University of Cincinnati In partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering of the College of Engineering and Applied Sciences July 2014 by Srikkanth Govindaraajan B.E. Electronics and Communication Engineering Anna University, 2011 Committee Chair: Dr. Carla Purdy Abstract Biometric technology is playing a vital role in the present day due to the rapid development of secure systems and home automation that have made our lives easier. But the question arises as to how far these systems are secure. With advances in hacking, the traditional username and password security protocol is not optimal for all security based systems. Though fingerprint identification systems provided a path-breaking solution, there are many methods to forge fingerprints. While other technologies like voice recognition, iris recognition, etc., co-exist, the security and safety of these technologies are also open to question. The major objective of this thesis is to provide enhanced security through a biometrics based embedded system using the technique of Vascular Pattern Recognition or Vein Pattern Recognition (VPR). Another objective is to enhance the vascular pattern image through various image processing techniques. Another target is to reduce the Comparison for Result (CFR) time by a significant factor. Finally, the aim is to implement this VPR based embedded system in a real time software environment. For the system we implemented, our experiments achieved a false accept rate of 0% and a false reject rate of 6.34%. Furthermore, it has been demonstrated in our research that the Speeded Up Robust Features (SURF) algorithm is faster than its predecessor algorithm Scale Invariant Feature Transform (SIFT). The principal conclusion of the thesis is that a safe and secure system can be developed on a small scale with precise results. Given the resources, this system could be extended to a larger scale and customized for a wide range of applications. Keywords: Vascular Pattern Recognition, Vein Pattern Recognition, image processing techniques, SURF algorithm, OpenCV. 2 Acknowledgements I would have never been able to complete my thesis without the guidance of my advisor, my committee members and help from my friends and support from my entire family. I would like to express my heartfelt gratitude and thanks to my advisor Dr.Carla Purdy for her patience, guidance and providing me with an excellent atmosphere for doing my research. Right from the beginning she has been very supportive and patient in answering my queries. I would also like to specially thank Dr.George Purdy and Dr.Wen-Ben Jone for accepting to be a part of my defense committee despite their busy schedule. I would like to thank all my friends who helped in giving their vein patterns that were used in the research and for their inputs and moral support throughout my Masters at University of Cincinnati. I would like to thank the University of Cincinnati and the Department of Electrical and Computer Engineering in specific for providing me with a great environment to learn and develop myself as an Engineer over the last few years. I would like to thank my parents Usha and Govindaraajan, my brother and my sister in law for their love, affection and support in all my endeavors. I would like to specially thank my wife’s family for their love and support. Finally, I would like to thank my wife, Sudha Badrinathan who has been very patient and caring throughout this arduous journey. She has stayed up late nights with me during this research and has motivated me throughout my Master’s degree. I thank god for giving me this life and opportunity to associate with wonderful people and blessing me for what I am today. - Srikkanth Govindaraajan 3 TABLE OF CONTENTS Abstract……………………………………………………………………………………………………………………………………………….2 Acknowledgements……………………………………………………………………………………………………………………………..3 List of Figures……………………………………………………………………………………………………………………………………….7 List of Tables……………………………………………………………………………………………………………………………………….10 1 INTRODUCTION………………………………………………………………………………………………………………….11 1.1 PROBLEM…………………………………………………………………………………………………………….11 1.2 OBJECTIVE……………………………………………………………………………………………………………12 1.3 APPROACH…………………………………………………………………………………………………………..12 1.4 ORGANIZATION……………………………………………………………………………………………………12 2 BACKGROUND AND RELATED WORK……………………………………………………………………………………13 2.1 EMBEDDED SYSTEMS……………………………………………………………………………………………13 2.2 BIOMETRICS AND ITS ROLE IN IDENTITY MANAGEMENT………………………………………14 2.2.1 CHARACTERESTICS OF BIOMETRIC SYSTEM…………………………………14 2.2.2 CONSIDERATIONS FOR A BIOMETRIC SYSTEM………………………………14 2.2.3 GENERAL WORKING OF A BIOMETRIC SYSTEM…………………………….14 2.2.4 PERFORMANCE METRICS OF A BIOMETRIC SYSTEM…………………….16 2.3 VASCULAR PATTERN RECOGNITION………………………………………………………………………17 2.3.1 VASCULAR PATTERN IN FINGERS AND HANDS………………………………19 2.4 THE OPENCV LIBRARY……………………………………………………………………………………………20 2.4.1 WHAT IS OPENCV?...........................................................................20 2.4.2 WHAT IS COMPUTER VISION?..........................................................21 2.4.3 HARDWARE FOR COMPUTER VISION SYSTEMS…………………………….22 2.5 PATTERN MATCHING IN BIOMETRICS…………………………………………………………………….22 2.5.1 FEATURE DETECTION ALGORITHMS………………………………………………23 2.5.2 TYPES OF IMAGE FEATURES………………………………………………………….23 2.5.3 FEATURE DESCIPTION…………………………………………………………………..26 2.5.4 SCALE INVARIANT FEATURE TRANSFORM (SIFT) ALGORITHM……….26 2.5.5 SPEEDED UP ROBUST FEATURES (SURF) ALGORITHM……………………29 2.6 RELATED WORK……………………………………………………………………………………………………..30 4 3 APPROACH……………………………………………………………………………………………………………………………33 3.1 BASIC CONCEPTS……………………………………………………………………………………………………33 3.1.1 VEIN DETECTION PROCESS……………………………………………………………33 3.1.2 IMAGE DATABASE…………………………………………………………………………34 3.2 HARDWARE SELECTION AND SETUP……………………………………………………………………….35 3.3 SOFTWARE REQUIREMENTS AND IMPLEMENTATION…………………………………………….38 3.3.1 THE OPEN SOURCE WORLD…………………………………………………………..38 3.3.2 OPERATING SYSTEM……………………………………………………………………..38 3.3.3 UBUNTU OPERATING SYSTEM………………………………………………………40 3.3.3.1 SYSTEM REQUIREMENTS FOR UBUNTU……………………..41 3.3.4 THE OPENCV LIBRARY…………………………………………………………………..41 3.4 OVERALL WORKING OF THE VASCULAR PATTERN RECOGNITION SYSTEM…………….42 3.5 FUNCTIONALITIES AND WORKING OF THE SYSTEM’S PROGRAM CODE…………………42 SYSTEM DIAGRAM AND FLOWCHARTS………………………………………………………………….45 4 RESULTS AND ANALYSIS……………………………………………………………………………………………………….50 4.1 ALGORITHM ANALYSIS…………………………………………………………………………………………..50 4.1.1 ONE TO ONE MATCHING………………………………………………………………50 4.1.2 ONE TO MANY MATCHING……………………………………………………………54 4.1.3 PERFORMANCE MEASURES………………………………………………………....57 4.2 AUXILIARY FUNCTIONS OF THE SYSTEM…………………………………………………………………59 4.2.1 VIEWING IMAGE ON THE DATABASE…………………………………………….59 4.2.2 PROCESS THE IMAGE…………………………………………………………………….59 4.2.3 ACCESSING THE USER MANUAL…………………………………………………….62 4.2.4 REMOVE IMAGE FROM DATABASE……………………………………………….62 4.2.5 CHANGING PASSWORD………………………………………………………………..64 4.2.6 ADDING AN IMAGE TO THE DATABASE…………………………………………65 4.3 DRAWBACKS AND LIMITATIONS…………………………………………………………………………….66 4.4 COMPARISON TO OTHER RELATED WORK……………………………………………………………..68 5 CONCLUSION AND FUTURE WORK………………………………………………………………………………………..69 5.1 SUMMARY OF WORK……………………………………………………………………………………………..69 5.2 FUTURE WORK……………………………………………………………………………………………………….69 5 Appendix A – Sample calculations for false reject rate…………………………………………………………………71 Appendix B – Sample calculations for average time taken to find an image in the database………..73 REFERENCES………………………………………………………………………………………………………………………………..77 6 LIST OF FIGURES Figure 1.1: Images of Hitachi’s, Fujitsu’s and M2SYS hand vein scanners [38][39][40]……………………………12 Figure 2.1: Basic structure of an embedded system [75]……………………………………………………………………….14 Figure 2.2: Biometric system model [5]…………………………………………………………………………………………………17 Figure 2.3: Arrangement for acquiring vascular pattern [74]…………………………………………………………………20 Figure2.4: Experimental setup and raw image obtained from the IR Camera………………………………………..20 Figure 2.5: OpenCV’s basic structure [22]………………………………………………………………………………………………22 Figure 2.6: Input and output of a corner detection algorithm. [29]………………………………………………………..25 Figure 2.7: Output of a blob detection algorithm [42]……………………………………………………………………………26 Figure 2.8: Sample image and its ridge detection output [28]………………………………………………………………..26 Figure 2.9: sample image (a) and its output (b) of a canny edge detector [44]………………………………….…..27 Figure 2.10: Stages of SIFT algorithm key points filtering [32]……………………………………………………………….28 Figure 2.11: Keypoints / Feature detectors using SURF [35]………………………………………………………………….30 Figure 2.12: Object Detection using SURF algorithm [35]………………………………………………………………………31 Figure 3.1: Optical Window for Vein Detection Process using Near Infrared Light [46]………………………….35 Figure 3.2: Interaction of Near Infrared Light on human skin [48]………………………………………………………….35 Figure 3.3: Sample Finger Vein Database images [49]……………………………………………………………………………36 Figure 3.4: Images of different LED illuminators [55]……………………………………………………………………………..37 Figure 3.5: Image of Sabrent Night Vision Webcam used in experiments………………………………………………38 Figure 3.6: Experimental hardware setup with Sabrent Night Vision Webcam………………………………………38 Figure 3.7: Interrupt sources and handling [59]…………………………………………………………………………………….40 Figure
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