Biometric Image Discrimination Technologies

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Idea Group Inc (IGI), Jan 1, 2006 - Computers - 358 pages
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Biometric Image Discrimination Technologies addresses highly relevant issues to many fundamental concerns of both researchers and practitioners of biometric image discrimination (BID) in biometric applications. This book describes the basic concepts necessary for a good understanding of BID and answers some important introductory questions about BID.
Biometric Image Discrimination Technologies covers the theories which are the foundations of basic BID technologies, while developing new algorithms which are verified to be more effective in biometrics authentication. This book will assist students new to the field and will also be useful to senior researchers in this area. This book is a part of the Computational Intelligence and its Applications Series.
 

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Contents

An Introduction to Biometrics Image Discrimination BID
1
Applications of Biometrics
5
Biometrics Systems and Discrimination Technologies
7
What are BID Technologies?
8
History and Development of BID Technologies
9
AppearanceBased BID Technologies
12
Principal Component Analysis
21
Definitions and Technologies
22
Approach Results
190
Experimental Results
196
Summary
202
Discriminant DCT Feature Extraction
205
Approach Definition and Description
206
Experiments and Analysis
213
Summary
220
Other Typical BID Improvements
222

NonLinear PCA Technologies
34
Summary
38
Linear Discriminant Analysis
41
LDA Definitions
49
NonLinear LDA Technologies
56
Summary
61
PCALDA Applications in Biometrics
65
Face Recognition
66
Palmprint Identification
80
Gait Application
95
Ear Biometrics
107
Speaker Identification
112
Iris Recognition
117
Signature Verification
123
Summary
130
Statistical Uncorrelation Analysis
139
Basic Definition
140
Uncorrelated Optimal Discrimination Vectors UODV
141
Improved UODV Approach
143
Experiments and Analysis
149
Summary
154
Solutions of LDA for Small Sample Size Problems
156
Overview of Existing LDA Regularization Techniques
158
A Unified Framework for LDA
159
A Combined LDA Algorithm for SSS Problem
164
Experiments and Analysis
171
Summary
184
An Improved LDA Approach
187
Definition and Notations
189
Dual Eigenspaces Method
223
PostProcessing on LDABased Method
225
Summary
232
Complete Kernel Fisher Discriminant Analysis
235
Theoretical Perspective of KPCA
237
KPCA Plus LDA
239
Complete KFD Algorithm
243
Experiments
248
Summary
255
2D Image MatrixBased Discriminator
258
2D Image MatrixBased PCA
259
2D Image MatrixBased LDA
274
Summary
284
TwoDirectional PCALDA
287
Basic Models and Definitions
290
TwoDirectional PCA Plus LDA
304
Experimental Results
307
Summary
324
Feature Fusion Using Complex Discriminator
329
Serial and Parallel Feature Fusion Strategies
331
Complex Linear Projection Analysis
332
Feature Preprocessing Techniques
335
Symmetry Property of Parallel Feature Fusion
337
Biometric Applications
339
Summary
348
About the Authors
351
Index
353
Copyright

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Page vii - knowledge" of the authorized person. This is why biometrics identification or verification system started to be more focused in the recent years.
Page 2 - The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.

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