Multispectral Biometrics Systems and Applications 1st Edition by David Zhang, Zhenhua Guo, Yazhuo Gong 3319224855 9783319224855

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ISBN-10 :  3319224855 

ISBN-13 : 9783319224855

Author:  David Zhang, Zhenhua Guo, Yazhuo Gong 

Describing several new biometric technologies, such as high-resolution fingerprint, finger-knuckle-print, multi-spectral backhand, 3D fingerprint, tongueprint, 3D ear, and multi-spectral iris recognition technologies, this book analyzes a number of efficient feature extraction, matching and fusion algorithms and how potential systems have been developed. Focusing on how to develop new biometric technologies based on the requirements of applications, and how to design efficient algorithms to deliver better performance, the work is based on the author’s research with experimental results under different challenging conditions described in the text. The book offers a valuable resource for researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, biometrics, and security applications, amongst others.

Multispectral Biometrics: Systems and Applications 1st Table of contents:

Part I Background of Multispectral Biometrics
1 Overview
Abstract
1.1 The Need for Biometrics
1.1.1 Biometrics System Architecture
1.1.2 Operation Mode of a Biometrics System
1.1.3 Evaluation of Biometrics and Biometrics System
1.2 Different Biometrics Technologies
1.2.1 Voice Recognition Technology
1.2.2 Signature Recognition Technology
1.2.3 Iris Recognition Technology
1.2.4 Face Recognition Technology
1.2.5 Fingerprint Recognition Technology
1.2.6 Palmprint Recognition Technology
1.2.7 Hand Geometry Recognition Technology
1.2.8 Palm Vein Recognition Technology
1.3 A New Trend: Multispectral Biometrics
1.4 Arrangement of This Book
References
2 Multispectral Biometrics Systems
Abstract
2.1 Introduction
2.2 Different Biometrics Technologies
2.2.1 Multispectral Iris
2.2.2 Multispectral Fingerprint
2.2.3 Multispectral Face
2.2.4 Multispectral Palmprint
2.2.5 Multispectral Dorsal Hand
2.3 Security Applications
2.4 Summary
References
Part II Multispectral Iris Recognition
3 Multispectral Iris Acquisition System
Abstract
3.1 System Requirements
3.2 Parameter Selection
3.2.1 Capture Unit
3.2.2 Illumination Unit
3.2.3 Interaction Unit
3.2.4 Control Unit
3.3 System Performance Evaluation
3.3.1 Proposed Iris Image Capture Device
3.3.2 Iris Database
3.3.3 Image Fusion and Recognition
3.4 Summary
References
4 Feature Band Selection for Multispectral Iris Recognition
Abstract
4.1 Introduction
4.2 Data Collection
4.2.1 Overall Design
4.2.2 Checkerboard Stimulus
4.2.3 Data Collection
4.3 Feature Band Selection
4.3.1 Data Organization of Dissimilarity Matrix
4.3.2 Improved (2D)2PCA
4.3.3 Low-Quality Evaluation
4.3.4 Agglomerative Clustering Based on the Global Principle
4.4 Experimental Results and Analysis
4.5 Summary
References
5 The Prototype Design of Multispectral Iris Recognition System
Abstract
5.1 Introduction
5.2 System Framework
5.2.1 Overall Design
5.2.2 Illumination Unit
5.2.3 Interaction Unit
5.2.4 Control Unit
5.3 Multispectral Image Fusion
5.3.1 Proposed Iris Image Capture Device
5.3.2 Iris Database
5.3.3 Score Fusion and Recognition
5.3.4 Experimental Results and Analysis
5.4 Summary
References
Part III Multispectral Palmprint Recognition
6 An Online System of Multispectral Palmprint Verification
Abstract
6.1 Introduction
6.2 The Online Multispectral Palmprint System Design
6.3 Multispectral Palmprint Image Analysis
6.3.1 Feature Extraction and Matching for Each Band
6.3.2 Inter-spectral Correlation Analysis
6.3.3 Score-Level Fusion Scheme
6.4 Experimental Results
6.4.1 Multispectral Palmprint Database
6.4.2 Palmprint Verification on Each Band
6.4.3 Palmprint Verification by Fusion
6.4.4 Anti-spoofing Test
6.4.5 Speed
6.5 Summary
References
7 Empirical Study of Light Source Selection for Palmprint Recognition
Abstract
7.1 Introduction
7.2 Multispectral Palmprint Data Collection
7.3 Feature Extraction Methods
7.3.1 Wide Line Detection
7.3.2 Competitive Coding
7.3.3 (2D)2PCA
7.4 Analyses of Light Source Selection
7.4.1 Database Description
7.4.2 Palmprint Verification Results by Wide Line Detection
7.4.3 Palmprint Verification Results by Competitive Coding
7.4.4 Palmprint Identification Results by (2D)2PCA
7.4.5 Discussions
7.5 Conclusion
References
8 Feature Band Selection for Online Multispectral Palmprint Recognition
Abstract
8.1 Introduction
8.2 Hyperspectral Palmprint Data Collection
8.3 Feature Band Selection by Clustering
8.4 Clustering Validation by Verification Test
8.5 Summary
References
Part IV Multispectral Hand Dorsal Recognition
9 Dorsal Hand Recognition
Abstract
9.1 Introduction
9.2 Multispectral Acquisition System and Database
9.2.1 Image Acquisition System
9.2.2 ROI Database
9.3 Feature Representation
9.3.1 Introduction of Dorsal Hand Feature Representation
9.3.2 (2D)2PCA
9.3.3 CompCode
9.3.4 MFRAT
9.4 Optimal Band Selection
9.4.1 Left–Right Comparison
9.4.2 Feature Comparison Result
9.4.3 Feature Estimation
9.4.4 Feature Fusion
9.4.5 Optimal Single Band
9.5 Summary
References
10 Multiple Band Selection of Multispectral Dorsal Hand
Abstract
10.1 Introduction
10.2 Correlation Measure
10.2.1 Feature Representation
10.2.2 Pearson Correlation
10.3 Band Clustering
10.3.1 Correlation Map Analysis
10.3.2 Model Setup
10.3.3 Clustering Methodology
10.3.4 Clustering Result
10.3.5 Parameter Analysis
10.4 Band Selection
10.4.1 Representative Band Selection
10.4.2 Fusion Results
10.4.3 Anti-spoof Test
10.5 Summary
References
11 Comparison of Palm and Dorsal Hand Recognition
Abstract
11.1 Introduction
11.2 Difference Analysis
11.2.1 Physiological Structure Difference
11.2.2 Spectral Character Difference
11.2.3 Other Difference
11.3 Comparison Experiment
11.3.1 Combined Database
11.3.2 Single Band Comparison
11.3.3 Multiple Bands Comparison
11.4 Summary
References
Part V Conclusion and Future Work
12 Book Review and Future Work
12.1 Book Recapitulation
12.2 Future Work
12.2.1 Sensor Size and Cost
12.2.2 Higher Performance
12.2.3 Distinctiveness
12.2.4 Permanence
12.2.5 Privacy Concerns

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