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E-raamat: Iris Image Recognition: Wavelet Filter-banks Based Iris Feature Extraction Schemes

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This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provides an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc. that will give the new directions of the research in the relevant field for the readers.

1 Introduction
1(22)
1.1 Biometrics
1(1)
1.2 Requirement of Biometrics Systems
2(1)
1.3 Iris as a Biometric
2(2)
1.4 Strengths and Weaknesses of the Iris as a Biometric
4(1)
1.4.1 Strengths of Iris Biometric
4(1)
1.4.2 Weaknesses of Iris Biometric
5(1)
1.5 Performance Measures of Iris Recognition System
5(1)
1.6 Nonideal Iris Recognition: A New Challenge
5(1)
1.7 A Brief Review On
6(11)
1.7.1 Iris Recognition Algorithms
7(6)
1.7.2 Two-Channel One-Dimensional Filter Banks
13(2)
1.7.3 Two-Dimensional Filter Banks
15(2)
1.8 Motivation
17(1)
1.9 Summary
18(5)
References
18(5)
2 Features Based on Triplet Half-Band Wavelet Filter-Banks
23(22)
2.1 Introduction
23(2)
2.2 Review of the Related Filter Banks
25(4)
2.2.1 Triplet Halfband Filter Bank
27(1)
2.2.2 Factorization Based on a Generalized Half-Band Polynomial
28(1)
2.3 Design of New Class of THFB
29(5)
2.3.1 Design Example
30(2)
2.3.2 Properties of the Designed THFB Desirable for Iris Feature Extraction
32(2)
2.4 Iris Recognition Algorithm
34(4)
2.4.1 Feature Extraction Using a New Class of THFB
34(3)
2.4.2 Design of k-out-of-n: A Post-classifier for Iris Recognition
37(1)
2.5 Experimental Results
38(4)
2.6 Summary
42(3)
References
42(3)
3 Combined Directional Wavelet Filter-Banks Based Features
45(14)
3.1 Introduction
45(1)
3.2 Review of the Related Directional Filter Bank
45(1)
3.3 Construction of the Directional Filter Bank
46(7)
3.3.1 Design of 1-D Biorthogonal Wavelet FB Using Factorization of an HBP
46(2)
3.3.2 Construction of 2-D Separable Filter Bank
48(1)
3.3.3 Construction of Fan Shaped Filter Bank
49(1)
3.3.4 Construction of Directional Wavelet Filter Bank
50(1)
3.3.5 Construction of Rotated Directional Wavelet Filter Bank
51(2)
3.4 Feature Extraction Using DWFB and RDWFB
53(2)
3.5 Experimental Results
55(2)
3.6 Summary
57(2)
References
58(1)
4 Iris Representation by Combined Hybrid Directional Wavelet Filter-Banks
59(10)
4.1 Introduction
59(1)
4.2 Review of the Related Filter Banks
60(1)
4.3 Design of Combined Hybrid Directional Wavelet FB
60(4)
4.3.1 Construction of 2-D Separable Filter Bank
60(1)
4.3.2 Design of the Triplet Halfband Fan Shaped Filter Bank
61(1)
4.3.3 1-D to 2-D Mapping
62(2)
4.4 Iris Feature Extraction Using CHDWFB
64(1)
4.5 Experimental Results
65(2)
4.6 Summary
67(2)
References
68(1)
5 Ordinal Measures Based on Directional Ordinal Wavelet Filters
69(14)
5.1 Introduction
69(1)
5.2 Review of the Related Filter Banks
70(1)
5.3 Design of Triplet Halfband Checkerboard Shaped Filter Bank (THCSFB)
71(4)
5.3.1 Design of Triplet 1-D Framework from Generalized HBPs
71(1)
5.3.2 1-D to 2-D Mapping
72(1)
5.3.3 Properties of the 1-D to 2-D Mapping
73(1)
5.3.4 Directional Extension of Wavelet Filter Bank
73(2)
5.4 Directional Ordinal Measures for Iris Recognition
75(1)
5.4.1 Introduction to Ordinal Measures
75(1)
5.4.2 Construction of Directional Ordinal Measures (DOMs)
76(1)
5.5 Iris Feature Extraction Using Proposed DOMs
76(2)
5.6 Experimental Results
78(3)
5.7 Summary
81(2)
References
82(1)
Appendix A 83