Doctor of Philosophy in Computer Science Srinivas
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A STUDY ON MULTIPLE METHODS OF FINGERPRINT HASH CODE GENERATION BASED ON MD5 ALGORITHM USING MODIFIED FILTERING TECHNIQUES AND MINUTIAE DETAILS Thesis submitted to Srinivas University in Partial Fulfillment of the requirements for the award of the Degree of DOCTOR OF PHILOSOPHY IN COMPUTER SCIENCE By Mr. Krishna Prasad K. Reg. No. SUPHDCOMSC2017/02 Under the Guidance of Dr. P. Sreeramana Aithal, Ph.D., Post Doc. Professor, College of Computer and Information Sciences, Srinivas University, Mangaluru-575001 SRINIVAS UNIVERSITY MUKKA, MANGALURU - 574 146 (KARNATAKA STATE), INDIA MARCH- 2018 Certificate i RESEARCH SUPERVISOR’S REPORT This is to certify that Thesis entitled “A Study on Multiple Methods of Fingerprint Hash Code Generation Based on MD5 Algorithm using Modified Filtering Techniques and Minutiae Details” Submitted to Srinivas University, Mukka, Mangaluru, Karnataka State, India, by Krishna Prasad K., for the award of degree of Doctor of Philosophy in Computer Science is a record of bonafide research work carried out by him under my supervision. The Thesis has reached the standard of the regulations for the degree and it has not been previously formed the basis for the award of any degree, diploma, associateship, fellowship or any other similar title to the candidate or any other person (s). Signature of the Research Supervisor Dr. P. Sreeramana Aithal Professor, College of Computer and Information Sciences, Place: Mangaluru City Campus, Pandeshwar-Mangaluru-575001 Date: 20-03-2018 Karnataka State, India ii Declaration iii Declaration I, Krishna Prasad K., hereby declare that the Thesis entitled “Study on Multiple Methods of Fingerprint Hash Code generation based on MD5 algorithm using Modifying Techniques and Minutiae details”, submitted to Srinivas University, Mukka, Mangaluru, Karnataka State, India, in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy in Computer Science is the record of original research work carried out by me during the period from 30-03-2010 to 31- 01-2018 under the supervision and guidance of Dr. P. Sreeramana Aithal, and has not formed the basis for the award of any degree, diploma, associate-ship, fellowship, or other similar title of any candidate in this or any other university or other similar institutions of higher learning. This Thesis is free from any kind of plagiarism. Place: Mangaluru Signature of the candidate Date: 20-03-2018 Mr. Krishna Prasad K., M.Sc (Information Science), M. Phil (CS), M. Tech (IT). iv Acknowledgement v Acknowledgement I wish to acknowledge several individuals who provided me with immeasurable help in the completion of this Thesis and degree. First of all, I derive immense pleasure in placing on record my deep sense of appreciation, gratitude and indebtedness to my Thesis supervisor, Prof. Dr. P. Sreeramana Aithal, Professor, College of Computer and Information Sciences, Srinivas University, City Campus, Pandeshwar, Mangaluru, for his all-round help in suggesting the problem, sustaining the interest, motivating, inspiring, and extending valuable guidance for the successful completion of the present investigation. Also, numerous discussions I had with him have increased my knowledge. I am happy for the love and support I have had from Sumana S., my wife and Abheeshta Krishna, my son through the years of dreams, and making life truly exciting. I would like to thank my parents who have blessed me with great tolerance. I would like to thank my Father-in-law, Mother-in-law, and brother-in-law, for their continuous support and motivation for completing my research work. I would like to thank my brother and his family for moral support. Without you all, things would have so much harder. I am wholeheartedly grateful to Sri. CA. A. Raghavendra Rao, President, A. Shama Rao Foundation, Mangalore and also Chancellor, Srinivas University, Mukka, Mangalore, for encouraging me by providing all facilities to carry out this work. I wish to express my sincere thanks to Dr. P. Srinivas Rao, Vice- president, A. Shama Rao Foundation, Mangalore and also Pro-Chancellor, Srinivas University, Mukka, Mangalore, for their support. I am thankful to Prof. P. Sridhara Acharya, Coordinator, BCA Department, College of Computer and Information Sciences, Srinivas University, City Campus, Mangalore, for moral support and encouragement. I also thank all my colleagues at Srinivas College, Pandeshwar, Mangalore, for their kind help and encouragement throughout the period of my research work. Last but not the least; I thank the Almighty, who has made my life blissful. May his name be exalted, honored and glorified. KRISHNA PRASAD K. vi Contents vii CONTENTS Chapter Title Page No. No. Certificate (i) Declaration (iii) Acknowledgement (v) List of Figures (xiii) List of Tables (xvii) Synopsis (xxi) 1 Introduction to Biometrics & Fingerprint Recognition System 1-44 1.1 Introduction 3-5 1.2 Biometric Technology 5-16 1.2.1 Types of Biometric Technology 8 1.2.2 Challenges of Biometric System 13 1.2.3 Vulnerabilities in a Biometric System 14 1.3 Fingerprint Biometrics 16-22 1.3.1 Basic principles of fingerprint technology 17 1.3.2 Applications Areas of fingerprint Biometric 18 1.3.3 Types of Fingerprints 19 1.4 Fingerprint Features 22-26 1.4.1 Level 1 Features 23 1.4.2 Level 2 Features 24 1.4.3 Level 3 Features 26 1.5 Fingerprint Template and Protection 26 1.6 Studies on Fingerprint Sensing Methods 28 1.6.1 Fingerprint Acquisition Methods 29 1.7 Matching Algorithms 31 1.8 Performance Matrices of Fingerprint Biometric System 32 1.9 Different Techniques used in Authentication Process 34 1.10 Problem Specification and Motivation 35 1.11 An ideal Authentication System 37 1.12 Organization of the Thesis 42 1.13 Chapter Summary 44 2 Review of Literature 45-90 2.1Introduction 47 2.2 Reviews on Biometric Technology and Security 47 2.3 Reviews on Fingerprint Recognition System 52-57 2.3.1 Henry Classification era of Fingerprint Recognition 55 viii 2.4 Basic Structure of Fingerprint 57 2.5 Fingerprint Individuality Probability Models 60 2.6 Fingerprint Enhancement Techniques 65-69 2.6.1 Contrast Adjustment 67 2.6.1.1 Histogram Modelling 67 2.6.2 Filtering Methods 68 2.6.2.1 Median Filtering 68 2.6.2.2 High Pass filtering 68 2.6.2.3 Weiner Filtering 68 2.6.2.4 Gabor Filtering 68 2.6.3 Binarisation and Thinning 69 2.7 Fingerprint Segmentation Techniques 69 2.8 Fingerprint Matching Algorithms 72-86 2.8. 1 Minutiae based Fingerprint Matching 73 2.8. 1.1 Binarised Image Minutiae Identification Techniques 73 2.8. 1.1.1 Non Skeletonised Binary Image 74 2.8.1.1.1.1 Chaincode Processing Method 74 2.8.1.1.1.2 Run Length Encoding Method 75 2.8.1.1. 2 Skeletonisation based Minutiae Extraction Method 78 2.8.1.1.2.1 Crossing Number Based Thinned Minutiae Extraction 78 Method 2.8.1.1.2.2 Morphology based Minutiae Extraction Method 80 2.8.1.2 Minutiae Extraction from Greyscale images Method 80 2.8.1.2.1 Minutiae Extraction by subsequent ridge flow lines 81 2.8.1.2.2 Fuzzy Techniques for minutiae extraction from a 82 grayscale image 2.8.2 Non-minutiae Based Matching 82 2.8.3 Correlation based Matching 84 2.8. 4 Ridge Feature Based Matching 85 2.8.5 Hybrid Methods 85 2.9 Template Protection Schema 86-88 2.9.1 Feature Transform 86 2.9. 2 Biometric Cryptosystems 87 2.9.3 Fingerprint Hash Function 88 2.10 Research Gap 89 2.11 Chapter Summary 90 3 Methodology and Fingerprint Image Preprocessing Techniques 91-145 3.1 Introduction 93 3.2 Objectives of the Research 94 3.3 Scope of the Research 95 3.4 Proposed Methodologies 96 3.5 Filtering the Contrast and Brightness of Fingerprint Image 103 3.6 Image Enhancement- 휏-Tuning Based Filtering Algorithm 106-116 (Proposed Method) 3.6.1 Tuning Based Filtering Algorithm-Procedure 108 ix 3.6.2 Workflow of Tuning based Filtering Algorithm 109 3.6.3 Analysis of Proposed Filtering Algorithm 110 3.7 Edge Detection Algorithms 116-112 3.7.1 Sobel Operator 117 3.7.2 Prewitt operator 118 3.7.3 Roberts operator 118 3.7.4 Laplacian of Gaussian (LoG) operator 119 3.7.5 Canny Operator 120 3.8 Fingerprint Segmentation 122-134 3.8.1 Surfeit Clipping based Segmentation Algorithm (Proposed 123 Modified Method) 3.8.2 Surfeit Clipping based Segmentation Algorithm-Procedure 124 3.8.3 Workflow for Surfeit Clipping based Segmentation Algorithm 126 3.8.4 Flowchart of Surfeit Clipping based Segmentation Algorithm 127 3.8.5 Analysis of Surfeit Clipping based Segmentation Algorithm 130 3.9 Fingerprint skeletonisation (Thinning) 134-145 3.9.1 Edge Prediction based Skelton formation 134 3.9.2 Edge Prediction based skeleton Formation Algorithm-Procedure 138 3.9.3 Workflow and Flowchart of Edge Prediction based skeleton 140 Formation Algorithm 3.9.4 Analysis of Edge Prediction based skeleton formation 143 Algorithm 3.10 Chapter Summary 145 4 Fingerprint Feature Extraction & Hash Code Creation Phase 146-190 4.1 Introduction 148 4.2 Preprocessing of Thinned image 149-153 4.2.1 Algorithm for Preprocessing of Thinned image 150 4.2.2 Workflow for Preprocessing of Thinned image 151 4.2.3 Flowchart for preprocessing of Thinned image 151 4.2.4 Analysis of Preprocessing of Thinned image 153 4.3 Feature Extraction Techniques 153-158 4.3.1 Crossing Number Theory 154 4.3.2 Minutiae Extraction Algorithm based on Crossing Number 155 4.3.3 Workflow and Flowchart for Minutiae extraction based on 156 Crossing Number 4.3.4 Analysis of Minutiae extraction based on Crossing Number 158 4.4 Post processing- Processing Minutiae Table 158-169 4.4.