Sinha Advances in Biometrics

Modern Methods and Implementation Strategies
1. Auflage 2019
ISBN: 978-3-030-30436-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 360 Seiten

This book provides a framework for robust and novel biometric techniques, along with implementation and design strategies. The theory, principles, pragmatic and modern methods, and future directions of biometrics are presented, along with in-depth coverage of biometric applications in driverless cars, automated and AI-based systems, IoT, and wearable devices. Additional coverage includes computer vision and pattern recognition, cybersecurity, cognitive computing, soft biometrics, and the social impact of biometric technology. The book will be a valuable reference for researchers, faculty, and practicing professionals working in biometrics and related fields, such as image processing, computer vision, and artificial intelligence.Highlights robust and novel biometrics techniquesProvides implementation strategies and future research directions in the field of biometricsIncludes case studies and emerging applications

Ganesh R. Sinha, PhD, is a member of the faculty at Myanmar Institute of Information Technology (MIIT), where he teaches electronics and communications engineering. Before joining MIIT, Dr. Sinha was a Professor in the Department of Electronics and Communications Engineering and as Dean (Internal Quality Assessment Cell) at CMR Technical Campus, Hyderabad, India. He completed his B.E. in Electronics Engineering and M.Tech. in Computer Technology with a Gold Medal from the National Institute of Technology, Raipur, India, and received his Ph.D. in Electronics & Telecommunication Engineering from Chhattisgarh Swami Vivekanand Technical University, Bhilai. He has been teaching and conducting research in his discipline for more than 19 years, and has published nearly 200 research papers with national and international journals and conferences. He is an editorial board member and active reviewer for a number of international journals, including IEEE Transactions on Image Processing, and has authored six books. His research interests include biometrics, medical image processing, deep learning, computer-aided design (CAD), and computer vision.

Autoren/Hrsg.

Sinha, G. R.

Inhaltsverzeichnis

1;Preface;6
2;Acknowledgment;8
3;Contents;9
4;Editor's Biography;11
5;1 Introduction to Biometrics and Special Emphasis on Myanmar Sign Language Recognition;13
5.1;1.1 Introduction and Background;13
5.2;1.2 Classification;16
5.3;1.3 Societal and Ethical Issues;17
5.3.1;1.3.1 Ethical Issues;18
5.4;1.4 Soft Biometrics;18
5.5;1.5 Biometric Standards, Protocols, and Databases;19
5.5.1;1.5.1 Standards;20
5.5.2;1.5.2 Protocols;21
5.5.3;1.5.3 Databases;21
5.6;1.6 Myanmar Sign Language Recognition;22
5.6.1;1.6.1 Sign Language Recognition;23
5.6.2;1.6.2 Myanmar Sign Language (MSL) Recognition and MIIT Database;23
5.6.3;1.6.3 MSL Implementation and Results;25
5.7;1.7 Conclusions;32
5.8;References;32
6;2 Handling the Hypervisor Hijacking Attacks on Virtual Cloud Environment;36
6.1;2.1 Introduction;36
6.2;2.2 Related Works;38
6.3;2.3 Background Theory of Proposed System;39
6.3.1;2.3.1 Virtualization Concept;39
6.3.2;2.3.2 Detection Method Based on Behavior Approach;40
6.3.2.1;2.3.2.1 Translation Lookaside Buffer TLB-Based Approach;41
6.3.2.2;2.3.2.2 CPU-Based Detection Approach;41
6.3.3;2.3.3 Hypervisor Model;41
6.3.3.1;2.3.3.1 OS Virtualization;42
6.3.3.2;2.3.3.2 Hardware Emulation;42
6.3.3.3;2.3.3.3 Para-Virtualization;43
6.3.4;2.3.4 Hypervisor Hijacking Thread Types;45
6.4;2.4 Serious Vulnerabilities in Virtualization;46
6.4.1;2.4.1 VM Sprawl;47
6.4.2;2.4.2 Hyper-jacking Attack;47
6.4.3;2.4.3 VM Escape Method;48
6.4.4;2.4.4 Denial-of-Service Attack;48
6.4.5;2.4.5 Incorrect VM Isolation;48
6.4.6;2.4.6 Unsecured VM Migration or VMotion;48
6.4.7;2.4.7 Host and Guest Vulnerabilities;48
6.5;2.5 Hacker Lifestyle;49
6.5.1;2.5.1 White Hat Hacker;49
6.5.2;2.5.2 Black Hat Hacker;49
6.5.3;2.5.3 Gray Hat Hacker;50
6.6;2.6 Cyber-Attack Lifecycle;50
6.6.1;2.6.1 Phase 1: Reconnaissance;50
6.6.2;2.6.2 Phase 2: Initial Compromise;50
6.6.3;2.6.3 Phase 3: Establish Foothold;51
6.6.4;2.6.4 Phase 4: Lateral Movement;51
6.6.5;2.6.5 Phase 5: Target Attainment;51
6.6.6;2.6.6 Phase 6: Ex-filtration, Corruption, and Disruption;51
6.6.7;2.6.7 Phase 7: Malicious Activities (Fig. 2.10);51
6.7;2.7 Implementation Framework for Protecting Mechanism;52
6.7.1;2.7.1 VM Creation of Virtual Network Configuration;52
6.7.2;2.7.2 Tested Methodology;54
6.8;2.8 Behavior-Based Analysis for Hypervisor Detection;56
6.9;2.9 Protecting and Mitigation Technique for System Hardening;57
6.10;2.10 Future Studies;59
6.11;2.11 Conclusion;60
6.12;References;60
7;3 Proposed Effective Feature Extraction and Selection for Malicious Software Classification;62
7.1;3.1 Introduction;62
7.2;3.2 Related Work;64
7.3;3.3 Malicious Software Family Classification System;66
7.3.1;3.3.1 Analyzing Malware Samples and Generating Reports;67
7.3.2;3.3.2 Labeling Malicious Samples;68
7.3.3;3.3.3 Extracting Malicious Features;69
7.3.4;3.3.4 Applying N-gram;70
7.3.5;3.3.5 Representing and Selecting Malicious Features;71
7.3.6;3.3.6 Classifying Malware vs Benign Using Machine Learning;72
7.4;3.4 Results and Discussion;73
7.5;3.5 Conclusion;78
7.6;References;81
8;4 Feature-Based Blood Vessel Structure Rapid Matching and Support Vector Machine-Based Sclera Recognition with Effective Sclera Segmentation;83
8.1;4.1 Introduction;83
8.2;4.2 Proposed Design Methodologies;86
8.2.1;4.2.1 Pre-processing Process;86
8.2.1.1;4.2.1.1 Iris Segmentation;87
8.2.1.2;4.2.1.2 Sclera Segmentation;88
8.2.1.3;4.2.1.3 Sclera Blood Vein Enhancement;89
8.2.2;4.2.2 Features Extraction;90
8.2.3;4.2.3 Features Training and Classification;91
8.2.3.1;4.2.3.1 By K-d Tree-Based Matching Identifier;91
8.2.3.2;4.2.3.2 By SVM Classifier;93
8.3;4.3 Experimental Results and Performance Comparisons;94
8.4;4.4 Conclusions;99
8.5;References;99
9;5 Different Parameter Analysis of Class-1 Generation-2 (C1G2) RFID System Using GNU Radio;101
9.1;5.1 Introduction;101
9.2;5.2 RFID EPC C1G2 Protocol;102
9.2.1;5.2.1 Representation of RFID EPC Protocol in GNU Radio;102
9.2.2;5.2.2 Representation of BER in GNU for C1G2 Protocol;103
9.3;5.3 Introduction to RFID Authentication Factor;104
9.3.1;5.3.1 Single-Factor Authentication;105
9.3.2;5.3.2 Multi-factor Authentication;105
9.3.3;5.3.3 RFID Factor Authentication Application;106
9.3.4;5.3.4 Biometric Hash Functions;107
9.4;5.4 Digital Modulation Scheme for RFID System;107
9.4.1;5.4.1 Binary Amplitude Shift Keying (ASK);108
9.4.2;5.4.2 Binary Frequency Shift Keying (BFSK);108
9.4.3;5.4.3 Phase Shift Key (PSK);108
9.4.3.1;5.4.3.1 Binary Phase Shift Key (BPSK);109
9.4.3.2;5.4.3.2 Quadrature Phase Shift Key (QPSK);109
9.4.3.3;5.4.3.3 Quadrature Amplitude Modulation (QAM);110
9.4.3.4;5.4.3.4 Analysis of Digital Modulation Schemes over AWGN Channel;110
9.4.4;5.4.4 PSK over AWGN Channel;110
9.4.4.1;5.4.4.1 QPSK over AWGN Channel;111
9.4.4.2;5.4.4.2 QAM over AWGN Channel;111
9.4.4.3;5.4.4.3 ASK over AWGN Channel;111
9.4.4.4;5.4.4.4 FSK over AWGN Channel;111
9.4.4.5;5.4.4.5 BPSK over AWGN Channel;114
9.4.4.6;5.4.4.6 QPSK over AWGN Channel;114
9.4.4.7;5.4.4.7 QAM over AWGN Channel;114
9.5;5.5 Proposed Methodology;114
9.6;5.6 Results and Discussion;118
9.6.1;5.6.1 Performance of Detection Methods;118
9.6.2;5.6.2 Bit Error Rate for Digital Modulation Techniques;124
9.7;5.7 Conclusion;125
9.8;References;125
10;6 Design of Classifiers;127
10.1;6.1 Introduction;127
10.2;6.2 Cognitive Principles;128
10.3;6.3 Classification Problem;129
10.4;6.4 Classifier Models;130
10.5;6.5 Self-Regulatory Resource Allocation Network (SRAN);130
10.6;6.6 Metacognitive Neural Network (McNN);131
10.6.1;6.6.1 Learning Strategies;132
10.6.2;6.6.2 Knowledge Measures;133
10.7;6.7 Metacognitive Fuzzy Inference System (McFIS);133
10.8;6.8 Projection-Based Learning with McNN (PBL McNN);134
10.9;6.9 Metacognitive Extreme Learning Machine (McELM);136
10.10;6.10 Summary;136
10.11;Bibliography;136
11;7 Social Impact of Biometric Technology: Myth and Implications of Biometrics: Issues and Challenges;139
11.1;7.1 Introduction;139
11.2;7.2 Biometric Myths and Misrepresentation;140
11.3;7.3 Vulnerability or Susceptibility Points of a Biometric System;145
11.4;7.4 Matter of Concerns of Biometrics;147
11.4.1;7.4.1 Biometric Framework Configuration Concerns;147
11.4.2;7.4.2 Confirmation;147
11.4.3;7.4.3 Liveness Detection;148
11.4.4;7.4.4 Collapse Rates;148
11.4.5;7.4.5 Circumvention and Repudiation;149
11.4.6;7.4.6 Handicapped Non-registrable Users;150
11.4.7;7.4.7 Adaptability;150
11.5;7.5 Impediments of Biometrics;151
11.6;7.6 Challenges, Difficulties, and Issues of Biometric System;153
11.6.1;7.6.1 Needs of Multi-Model Biometrics;153
11.7;7.7 Conclusion;163
11.8;References;164
12;8 Segmentation and Classification of Retina Images Using Wavelet Transform and Distance Measures;166
12.1;8.1 Introduction;166
12.2;8.2 Structure of the Eye;168
12.2.1;8.2.1 Lexicon of Terms for the Eye;169
12.2.2;8.2.2 Facts of Diabetic Eye Disease;171
12.3;8.3 Symptoms and Detection;171
12.3.1;8.3.1 Retinal Analysis;173
12.3.2;8.3.2 Causes of an Occlusion;173
12.3.3;8.3.3 Risk Factors of Retinal Vessel Occlusion;174
12.3.4;8.3.4 Blood Vessels;175
12.3.5;8.3.5 Exudates;177
12.3.6;8.3.6 Microaneurysms;177
12.3.7;8.3.7 Retinal Hemorrhage;178
12.4;8.4 Literature Review;179
12.4.1;8.4.1 Our Contribution;183
12.5;8.5 Proposed Methodology;183
12.5.1;8.5.1 Disease Classification;186
12.5.2;8.5.2 Datasets;186
12.5.3;8.5.3 Distance Measures;187
12.6;8.6 Results and Discussion;188
12.7;8.7 Conclusion;190
12.8;References;190
13;9 Language-Based Classification of Document Images Using Hybrid Texture Features;192
13.1;9.1 Introduction;192
13.2;9.2 Literature Review;197
13.3;9.3 Proposed Methodology;198
13.3.1;9.3.1 Preprocessing;198
13.3.2;9.3.2 Proposed Hybrid Texture Features;201
13.3.2.1;9.3.2.1 Stationary Wavelet Transform (SWT);201
13.3.2.2;9.3.2.2 Histogram of Oriented Gradients (HOG);206
13.3.3;9.3.3 SVM Classifier;209
13.4;9.4 Experimental Results;210
13.5;9.5 Conclusion;215
13.6;References;217
14;10 Research Trends and Systematic Review of Plant Phenotyping;219
14.1;10.1 Introduction;219
14.2;10.2 Related Work;223
14.3;10.3 Experimental Setup;225
14.3.1;10.3.1 Dataset Gathering;225
14.3.2;10.3.2 Preprocessing Module;226
14.3.3;10.3.3 Height Calculation Module;226
14.3.4;10.3.4 Region of Interest Calculation;227
14.4;10.4 Results and Discussion;227
14.5;10.5 Conclusion and Future Work;231
14.6;References;232
15;11 Case Studies on Biometric Application for Quality-of-Experience Evaluation in Communication;234
15.1;11.1 Introduction;234
15.2;11.2 Experimental Setting;236
15.2.1;11.2.1 Game for Experiment;236
15.2.2;11.2.2 Experiment Preparation;236
15.2.3;11.2.3 Subjective Evaluation Experiment Procedure;240
15.3;11.3 EEG Measurement;240
15.3.1;11.3.1 EEG Measurement Apparatus and Measurement Points;240
15.3.2;11.3.2 EEG Frequency Bands and Significance;240
15.3.3;11.3.3 EEG Power Spectrum;243
15.4;11.4 Experimental Results;243
15.4.1;11.4.1 EEG Power Spectrum Comparison;243
15.4.2;11.4.2 Average Increase Rate of Each Waveform;247
15.4.3;11.4.3 Average Increase Rate of Each Electrode Positions;248
15.4.4;11.4.4 Comprehensive Comparison;248
15.4.5;11.4.5 Player Level Comparison;249
15.4.6;11.4.6 Chinese and Japanese Comparison;251
15.5;11.5 Conclusions;253
15.6;References;253
16;12 Nearest Neighbor Classification Approach for Bilingual Speaker and Gender Recognition;255
16.1;12.1 Introduction;255
16.1.1;12.1.1 Variants of Speaker Recognition;257
16.2;12.2 Applications of Speech Processing and Speaker Recognition;258
16.3;12.3 Limitations of Speaker Recognition;260
16.4;12.4 Issues and Challenges;260
16.5;12.5 Related Work;262
16.6;12.6 Proposed Method;263
16.6.1;12.6.1 Dataset;263
16.6.2;12.6.2 Flow Chart;263
16.6.3;12.6.3 Methodology;264
16.7;12.7 Results;268
16.8;12.8 Conclusions;270
16.9;References;270
17;13 Effective Security and Access Control Framework for Multilevel Organizations;273
17.1;13.1 Introduction;273
17.1.1;13.1.1 Introduction;273
17.1.2;13.1.2 Objectives of the Proposed System;274
17.2;13.2 Related Work;274
17.3;13.3 Background Theory;275
17.3.1;13.3.1 What Are External and Internal Threats?;275
17.3.2;13.3.2 Security Control Model;276
17.3.3;13.3.3 Bell-LaPadula Model;276
17.3.4;13.3.4 Integrity;277
17.3.5;13.3.5 Levels of Integrity;277
17.3.6;13.3.6 Strict Integrity Policy;277
17.3.7;13.3.7 Strict Integrity Access Control Model;278
17.3.8;13.3.8 Conditions of Integrity;278
17.3.9;13.3.9 Levels of Entity to Control Security;278
17.4;13.4 Access Control;280
17.4.1;13.4.1 Identification;280
17.4.2;13.4.2 Account Authentication;280
17.4.3;13.4.3 Authorization;280
17.4.4;13.4.4 Accountability;280
17.4.5;13.4.5 Types of Access Control;281
17.5;13.5 Biba Security Model;281
17.5.1;13.5.1 Access Modes of Biba Model;282
17.5.2;13.5.2 Policies of Biba Model;282
17.5.3;13.5.3 Advantages and Disadvantages of Biba Model;282
17.6;13.6 Security Controls;283
17.6.1;13.6.1 Account Authentication Control;283
17.6.2;13.6.2 Handling User Access Control;283
17.6.3;13.6.3 Using User Input Control;284
17.6.4;13.6.4 Handling Communication and Data Transfer Controls;284
17.6.5;13.6.5 How to Handle Employees' Daily Operations Controls?;284
17.7;13.7 What Is Multilevel Organization?;284
17.8;13.8 Secure Framework for Multilevel Organizations;285
17.8.1;13.8.1 Secure Framework for Multilevel Organizations (SFMO);285
17.8.2;13.8.2 Features of Secure Framework for Multilevel Organizations (FSFMO);286
17.8.3;13.8.3 Design of Proposed System;288
17.8.4;13.8.4 System Flow of Proposed System;289
17.8.5;13.8.5 Case Study for Multilevel Organizations;289
17.9;13.9 Privacy Policies at Workplace;292
17.10;13.10 Conclusion;293
17.11;References;294
18;14 Dimensionality Reduction and Feature Matching in Functional MRI Imaging Data;295
18.1;14.1 Introduction;295
18.2;14.2 Existing Works;298
18.3;14.3 Methods;301
18.3.1;14.3.1 Sliced Inversed Regression;301
18.3.2;14.3.2 Principal Component-Sliced Inverse Regression;302
18.3.3;14.3.3 F-magnetic Resonance Imaging Pain Prediction;302
18.3.3.1;14.3.3.1 Experimental Design;302
18.3.3.2;14.3.3.2 PC-SIR Analyses;303
18.3.3.3;14.3.3.3 Pain Prediction;303
18.3.4;14.3.4 Manifold Learning;303
18.3.5;14.3.5 Manifold Embedding and Regularization;304
18.3.5.1;14.3.5.1 M-Magnetic Resonance ImagingR1 (Affine Combination);305
18.3.5.2;14.3.5.2 M-Magnetic Resonance ImagingR2;305
18.3.5.3;14.3.5.3 Manifold Regularization and Reconstruction;306
18.3.6;14.3.6 An Initial Image Processing;307
18.3.7;14.3.7 Image Segmentation;307
18.3.8;14.3.8 Classification Methods;309
18.4;14.4 Conclusion;309
18.5;References;310
19;15 Classification of Biometrics and Implementation Strategies;312
19.1;15.1 Introduction;312
19.2;15.2 Traits;313
19.2.1;15.2.1 Physiological Biometric Traits;313
19.2.2;15.2.2 Behavior Biometric Traits;315
19.3;15.3 Classification of Biometric System;317
19.3.1;15.3.1 Unimodal Biometric System;317
19.3.2;15.3.2 Multimodal Biometric System;318
19.4;15.4 Implementation Strategies;318
19.5;15.5 Set of Features;320
19.6;15.6 Information Representation Through Features;321
19.6.1;15.6.1 Training and Testing Involved in Biometric System;322
19.6.2;15.6.2 Principal Component Analysis for Face Recognition;322
19.6.3;15.6.3 Eigenimage for Ear Recognition;324
19.6.4;15.6.4 Hamming Distance-Based Iris Recognition;324
19.6.5;15.6.5 Haar Wavelet Transform for Foot Recognition;326
19.7;15.7 Template Representation;326
19.7.1;15.7.1 Face Recognition;327
19.7.2;15.7.2 Ear Recognition;331
19.7.3;15.7.3 Iris Recognition;333
19.7.4;15.7.4 Footprint Recognition;335
19.8;Bibliography;336
20;16 Advances in 3D Biometric Systems;338
20.1;16.1 Introduction;338
20.2;16.2 Developments in 3D Biometric Systems;339
20.2.1;16.2.1 Face Recognition;339
20.2.2;16.2.2 Fingerprint Recognition;341
20.2.3;16.2.3 Iris Recognition;342
20.3;16.3 Anti-spoofing;343
20.3.1;16.3.1 Face Anti-spoofing;344
20.3.2;16.3.2 Fingerprint Anti-spoofing;347
20.3.3;16.3.3 Iris Anti-spoofing;347
20.4;16.4 Open-Source Softwares;348
20.5;16.5 Conclusions;349
20.6;References;349
21;Index;352