Biometric Image Discrimination Technologies

290 Pages·14.38 MB·English

by  Zhang D., Jing X., Yang, J.

Biometric Image Discrimination Technologies
by David Zhang, Xiaoyuan Jing and Jian Yang
Idea Group Publishing © 2006 (374 pages)
ISBN:159140830X
With a focus on linear projection analysis, the issues this book addresses are highly relevant to many fundamental concerns of both
researchers and practitioners of BID in biometric applications.

Biometric Image Discrimination Technologies
David Zhang
Biometrics Research Centre, The Hong Kong Polytechnic University,
Hong Kong
Xiaoyuan Jing
Bio-Computing Research Centre, ShenZhen Graduate School of Harbin Institute of Technology,
China
Jian Yang
Biometrics Research Centre, The Hong Kong Polytechnic University,
Hong Kong
IDEA GROUP PUBLISHING
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Library of Congress Cataloging-in-Publication Data
Zhang, David, 1949-
1
Biometric image discrimination technologies / David Zhang, Xiaoyuan Jing and Jian Yang.
p. cm.
Summary: "The book gives an introduction to basic biometric image discrimination technologies including
theories that are the foundations of those technologies and new algorithms for biometrics
authentication"—Provided by publisher.
Includes bibliographical references and index.
1-59140-830-X
(hardcover)—ISBN 1-59140-831-8 (softcover)—ISBN 1-59140-832-6 (ebook)
1. Pattern recognition systems. 2. Identification—Automation. 3. Biometric identification. I. Jing, Xiaoyuan.
II. Yang, Jian. III. Title.
TK7882.P3Z 44 2006
006.4—dc22
2005032048
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this
book are those of the authors, but not necessarily of the publisher.
About the Authors
David Zhang graduated in computer science from Peking University (1974). He earned his MSc and PhD
in computer science from the Harbin Institute of Technology (HIT) (1982 and 1985, respectively). From
1986 to 1988, he was a postdoctoral fellow at Tsinghua University and then an associate professor at the
Academia Sinica, Beijing. In 1994, he received his second PhD in electrical and computer engineering
from the University of Waterloo, Ontario, Canada. Currently, he is a chair professor at The Hong Kong
Polytechnic University, where he is the founding director of the Biometrics Technology Centre
(UGC/CRC) supported by the Hong Kong SAR government. He also serves as an adjunct professor at
Tsinghua University, Shanghai Jiao Tong University, Beihang University, HIT and the University of
Waterloo. He is founder and editor-in-chief of the International Journal of Image and Graphics (IJIG),
book editor of the Kluwer International Series on Biometrics (KISB), and program chair of the International
Conference on Biometrics Authentication (ICBA). He is also the associate editor of more than 10
international journals, including IEEE Transactions on SMC-A/SMC-C and Pattern Recognition. He is the
author of more than 10 books and 160 journal papers related to his research areas. These include
biometrics, image processing, and pattern recognition. He is a current Croucher senior research fellow
and distinguished speaker of the IEEE Computer Society.
Xiaoyuan Jing graduated in computer application from the Jiangsu University of Science and
Technology (1992). He earned his MSc and PhD in pattern recognition from the Nanjing University of
Science and Technology (1995 and 1998, respectively). From 1998 to 2001, he was a manager of the
Image Technology Department of E-Com Company. From 2001 to 2004, he was an associate professor
at the Institute of Automation, Chinese Academia of Sciences, Beijing, and a visiting scholar at Hong
Kong Polytechnic University and Hong Kong Baptist University. Currently, he is a professor and doctor
supervisor at ShenZhen Graduate School of Harbin Institute of Technology, Shenzhen, China. He serves
as a member of the Intelligent Systems Applications Committee of IEEE Computational Intelligence
Society. He is a reviewer of several international journals such as IEEE Transactions and Pattern
Recognition. His research interests include pattern recognition, computer vision, image processing,
information fusion, neural network and artificial intelligence.
Jian Yang was born in Jiangsu, China, June 1973. He earned his BS in mathematics at the Xuzhou
Normal University (1995). He then completed an MS degree in applied mathematics at the Changsha
Railway University (1998) and his PhD at the Nanjing University of Science and Technology (NUST) in
the Department of Computer Science on the subject of pattern recognition and intelligence systems
(2002). In 2003, he was a postdoctoral researcher at the University of Zaragoza. In the same year, he
was awarded the RyC program Research Fellowship, sponsored by the Spanish Ministry of Science and
Technology. Currently, he is a professor in the Department of Computer Science of NUST and a
postdoctoral research fellow at The Hong Kong Polytechnic University. He is the author of more than 30
2
scientific papers in pattern recognition and computer vision. His current research interests include pattern
recognition, computer vision and machine learning.
Acknowledgments
Our sincere thank goes to professor Zhaoqi Bian of Tsinghua University, Beijing, and professor Jingyu
Yang of Najing Polytechnic University, Najing, China, for their advice throughout this research. We would
like to thank our team members, Dr. Hui Peng, Wangmeng Zuo, Dr. Guangming Lu, Dr. Xiangqian Wu,
Dr. Kuanquan Wang and Dr. Jie Zhou for their hard work and unstinting support. In fact, this book is the
common result of their many contributions. We would also like to express our gratitude to our research
fellows, Michael Wong, Laura Liu and Dr. Ajay Kumar for their invaluable help and support. Thanks are
also due to Martin Kyle, Dr. Zhizhen Liang, Miao Li and Xiaohui Wang for their help in the preparation of
this book. The financial support of the CERG fund from the HKSAR Government, the central fund from
the Hong Kong Polytechnic University and NFSC funds (No. 60332010 and No. 60402018) in China are,
of course, also greatly appreciated. We owe a debt of thanks to Jan Travers and Kristin Roth of Idea
Group Inc., for their initiative in publishing this volume.
David Zhang, Biometrics Research Centre
The Hong Kong Polytechnic University, Hong Kong
E-mail: [email protected]
Xiaoyuan Jing, Bio-Computing Research Centre
ShenZhen Graduate School of Harbin Institute of Technology, China
E-mail address: [email protected]
Jian Yang, Biometrics Research Centre
The Hong Kong Polytechnic University, Hong Kong
E-mail address: [email protected]
3
Contents
Biometric Image Discrimination Technologies ........................................................... 1
Contents ........................................................................................................................................ 4
Preface ........................................................................................................................................... 9
Chapter I: An Introduction to Biometrics Image Discrimination (BID).................... 14
DEFINITION OF BIOMETRICS TECHNOLOGIES .................................................................. 14
Finger-Scan Technology........................................................................................................ 14
Voice-Scan Technology ........................................................................................................ 15
Face-Scan Technology .......................................................................................................... 15
Palm-Scan Technology .......................................................................................................... 15
Iris-Scan Technology ............................................................................................................. 15
Signature-Scan Technology ................................................................................................... 16
Multiple Authentication Technologies .................................................................................. 16
APPLICATIONS OF BIOMETRICS ........................................................................................... 16
Law Enforcement .................................................................................................................. 16
Banking .................................................................................................................................. 16
Computer Systems (or Logical Access Control) ................................................................... 17
Physical Access ..................................................................................................................... 17
Benefit Systems ..................................................................................................................... 17
Immigration ........................................................................................................................... 17
National Identity .................................................................................................................... 17
Telephone Systems ................................................................................................................ 17
Time, Attendance and Monitoring ........................................................................................ 17
BIOMETRICS SYSTEMS AND DISCRIMINATION TECHNOLOGIES ................................ 17
WHAT ARE BID TECHNOLOGIES? ......................................................................................... 18
High Dimensionality ............................................................................................................. 18
Large Scale ............................................................................................................................ 19
Small Sample Size ................................................................................................................. 19
HISTORY AND DEVELOPMENT OF BID TECHNOLOGIES ................................................ 19
OVERVIEW: APPEARANCE-BASED BID TECHNOLOGIES ................................................ 21
BOOK PERSPECTIVE ................................................................................................................. 22
References ............................................................................................................................. 23
Section I: BID Fundamentals ........................................................................................ 29
Chapter II: Principal Component Analysis ................................................................ 30
INTRODUCTION ......................................................................................................................... 30
DEFINITIONS AND TECHNOLOGIES ..................................................................................... 30
Mathematical Background of PCA ....................................................................................... 30
Principal Component Analysis (PCA) ................................................................................... 33
NON-LINEAR PCA TECHNOLOGIES ...................................................................................... 39
An Introduction to Kernel PCA ............................................................................................. 39
Background Mathematics ...................................................................................................... 40
Methods ................................................................................................................................. 40
SUMMARY .................................................................................................................................. 42
References ............................................................................................................................. 42
Chapter III: Linear Discriminant Analysis ....................................................................... 45
INTRODUCTION ......................................................................................................................... 45
The Two-Category Case ........................................................................................................ 45
The Multicategory Case......................................................................................................... 47
Generalized Linear Discriminant Functions .......................................................................... 48
LDA DEFINITIONS ..................................................................................................................... 50
Fisher Linear Discriminant .................................................................................................... 50
Multiple Discriminant Analysis ............................................................................................ 53
4
NON-LINEAR LDA TECHNOLOGIES ...................................................................................... 56
SUMMARY .................................................................................................................................. 59
References ............................................................................................................................. 59
Chapter IV: PCA/LDA Applications in Biometrics .................................................... 62
INTRODUCTION ......................................................................................................................... 62
FACE RECOGNITION ................................................................................................................. 62
Eigenface ............................................................................................................................... 63
Fisherface............................................................................................................................... 64
Experimental Results ............................................................................................................. 66
PALMPRINT IDENTIFICATION ............................................................................................... 72
Fisherpalm ............................................................................................................................. 72
Eigenpalm .............................................................................................................................. 79
GAIT APPLICATION .................................................................................................................. 83
Overview of Approach .......................................................................................................... 83
Feature Extraction.................................................................................................................. 84
Recognition ............................................................................................................................ 87
Experiments ........................................................................................................................... 88
Remarks ................................................................................................................................. 91
EAR BIOMETRICS ...................................................................................................................... 91
Introduction ........................................................................................................................... 91
PCA in Ear Recognition ........................................................................................................ 93
Remarks ................................................................................................................................. 94
SPEAKER IDENTIFICATION .................................................................................................... 94
Introduction ........................................................................................................................... 94
Eigenspace Training Techniques ........................................................................................... 95
Experiments ........................................................................................................................... 96
Remarks ................................................................................................................................. 98
IRIS RECOGNITION ................................................................................................................... 98
Introduction ........................................................................................................................... 98
Image Preprocessing .............................................................................................................. 99
Feature Extraction.................................................................................................................. 99
Remarks ............................................................................................................................... 102
SIGNATURE VERIFICATION ................................................................................................. 103
Introduction ......................................................................................................................... 103
Signature Processing and Segmentation .............................................................................. 103
Flexible Matching and Stable Segments Extraction ............................................................ 105
PCA and MCA .................................................................................................................... 106
Experiment Results .............................................................................................................. 107
Remarks ............................................................................................................................... 107
SUMMARY ................................................................................................................................ 108
References ........................................................................................................................... 108
Section II: Improved BID Technologies ................................................................ 116
Chapter V: Statistical Uncorrelation Analysis ........................................................ 117
INTRODUCTION ....................................................................................................................... 117
BASIC DEFINITION .................................................................................................................. 117
UNCORRELATED OPTIMAL DISCRIMINATION VECTORS ............................................. 118
Fisher Vector ....................................................................................................................... 118
Foley-Sammon Discriminant Vectors ................................................................................. 118
Uncorrelated Discriminant Vectors ..................................................................................... 118
A Theorem on UODV ......................................................................................................... 119
IMPROVED UODV APPROACH ............................................................................................. 119
Approach Description .......................................................................................................... 119
5
Generalized UODV Theorem .............................................................................................. 121
EXPERIMENTS AND ANALYSIS ........................................................................................... 124
Experiments on 1D Data...................................................................................................... 125
Experiments on 2D Data...................................................................................................... 126
SUMMARY ................................................................................................................................ 127
References ........................................................................................................................... 127
Chapter VI: Solutions of LDA for Small Sample Size Problems ............................ 129
INTRODUCTION ....................................................................................................................... 129
OVERVIEW OF EXISTING LDA REGULARIZATION TECHNIQUES ............................... 130
A UNIFIED FRAMEWORK FOR LDA .................................................................................... 131
Theoretical Framework for LDA in Singular Case ............................................................. 131
Essence of LDA in SSS Cases ............................................................................................. 134
A COMBINED LDA ALGORITHM FOR SSS PROBLEM ..................................................... 134
Strategy of Finding Two Categories of Optimal Discriminant Vectors .............................. 135
Properties of the Two Categories of Optimal Discriminant Vectors................................... 137
Combined LDA Algorithm (CLDA) ................................................................................... 138
Comparison to Existing LDA Methods ............................................................................... 138
EXPERIMENTS AND ANALYSIS ........................................................................................... 140
Experiment Using the ORL Database ................................................................................. 140
Experiment Using the NUST603 Database ......................................................................... 147
Experimental Conclusions and Analysis ............................................................................. 150
SUMMARY ................................................................................................................................ 151
References ........................................................................................................................... 151
Chapter VII: An Improved LDA Approach ............................................................... 153
INTRODUCTION ....................................................................................................................... 153
DEFINITIONS AND NOTATIONS ........................................................................................... 154
APPROACH DESCRIPTION ..................................................................................................... 155
Improving the Selection of Discrimination Vectors ............................................................ 155
Improving the Statistical Uncorrelation of Discrimination Vectors .................................... 155
Improving the Selection of Principal Components ............................................................. 156
ILDA Approach ................................................................................................................... 158
EXPERIMENTAL RESULTS .................................................................................................... 159
Introduction of Databases .................................................................................................... 159
Experiments on the Improvement of Discrimination Vectors Selection ............................. 160
Experiments on the Improvement of Statistical UODV ...................................................... 161
Experiments on the Improvement of Principal Components Selection ............................... 161
Experiments on All of the Improvements............................................................................ 162
SUMMARY ................................................................................................................................ 163
References ........................................................................................................................... 164
Chapter VIII: Discriminant DCT Feature Extraction ................................................ 166
INTRODUCTION ....................................................................................................................... 166
APPROACH DEFINITION AND DESCRIPTION ................................................................... 166
Select DCT Frequency Bands by Using a 2D Separability Judgment ................................ 166
Recognition Procedure ........................................................................................................ 168
Theoretical Analysis ............................................................................................................ 169
EXPERIMENTS AND ANALYSIS ........................................................................................... 171
Experiments with the Yale Face Database .......................................................................... 171
Experiments with the ORL Face Database .......................................................................... 173
Experiments with the Palmprint Database........................................................................... 174
Analysis of Threshold Setting ............................................................................................. 175
SUMMARY ................................................................................................................................ 177
References ........................................................................................................................... 177
6
Chapter IX: Other Typical BID Improvements ......................................................... 179
INTRODUCTION ....................................................................................................................... 179
DUAL EIGENSPACES METHOD ............................................................................................ 179
Introduction to TEM ............................................................................................................ 179
Algebraic Features Extraction ............................................................................................. 179
Face Recognition Phase ....................................................................................................... 180
Experimental Results ........................................................................................................... 181
POST-PROCESSING ON LDA-BASED METHOD ................................................................. 181
Introduction ......................................................................................................................... 181
LDA-Based Facial Feature Extraction Methods ................................................................. 181
Post-Processing on Discriminant Vectors ........................................................................... 182
Experimental Results and Discussions ................................................................................ 183
SUMMARY ................................................................................................................................ 186
References ........................................................................................................................... 186
Section III: Advanced BID Technologies ............................................................. 188
Chapter X: Complete Kernel Fisher Discriminant Analysis ................................... 189
INTRODUCTION ....................................................................................................................... 189
THEORETICAL PERSPECTIVE OF KPCA ............................................................................. 190
A NEW KFD ALGORITHM FRAMEWORK: KPCA PLUS LDA .......................................... 191
Fundamentals ............................................................................................................................... 191
Strategy for Finding Fisher Optimal Discriminant Vectors in Feature Space ..................... 192
Idea of Calculating Fisher Optimal Discriminant Vectors .................................................. 193
A Concise KFD Framework: KPCA Plus LDA .................................................................. 194
COMPLETE KFD ALGORITHM .............................................................................................. 195
Extraction of Two Kinds of Discriminant Features............................................................. 195
Fusion of Two Kinds of Discriminant Features for Classification ...................................... 196
Complete KFD Algorithm ................................................................................................... 197
Relationship to Other KFD (or LDA) Algorithms .............................................................. 197
EXPERIMENTS .......................................................................................................................... 198
SUMMARY ................................................................................................................................ 204
References ........................................................................................................................... 205
Chapter XI: 2D Image Matrix-Based Discriminator ................................................. 207
INTRODUCTION ....................................................................................................................... 207
2D IMAGE MATRIX-BASED PCA .......................................................................................... 207
IMPCA Method ................................................................................................................... 207
IMPCA-Based Image Reconstruction ................................................................................. 208
Relationship to PCA ............................................................................................................ 208
Minimal Mean-Square Error Property of IMPCA ............................................................... 209
Comparison of PCA-and 2DPCA-Based Image Recognition Systems ............................... 211
Experiments and Analysis ................................................................................................... 213
2D IMAGE MATRIX-BASED LDA .......................................................................................... 219
Fundamentals ....................................................................................................................... 219
Orthogonal IMLDA (O-IMLDA) ........................................................................................ 221
Uncorrelated IMLDA (U-IMLDA) ..................................................................................... 222
Correlation Analysis ............................................................................................................ 223
Experiments and Analysis ................................................................................................... 224
SUMMARY ................................................................................................................................ 226
References ........................................................................................................................... 227
Chapter XII: Two-Directional PCA/LDA ................................................................... 229
INTRODUCTION ....................................................................................................................... 229
BDPCA Method .................................................................................................................. 229
BDPCA Plus LDA Method ................................................................................................. 230
7
BASIC MODELS AND DEFINITIONS .................................................................................... 231
Classical PCA's Over-Fitting Problem ................................................................................ 231
Previous Work in Solving PCA's Over-Fitting Problem ..................................................... 232
Modular PCA ............................................................................................................................... 234
BDPCA with Assembled Matrix Distance Metric .............................................................. 234
Overview of PCA Techniques for 2D Image Transform ............................................................ 238
General Idea of 2D Image Transform.................................................................................. 238
Holistic PCA: Inseparable Image Model Based Technique ................................................ 239
2D-KLT: Separable Image Model Based Technique ........................................................... 240
BDPCA: A Face-Image-Specific 2D-KLT Technique ........................................................ 240
TWO-DIRECTIONAL PCA PLUS LDA ................................................................................... 241
BDPCA +LDA: A New Strategy for Facial Feature Extraction ......................................... 241
EXPERIMENTAL RESULTS .................................................................................................... 243
Experiments with the ORL Database for BDPCA .............................................................. 243
Experiments with the PolyU Palmprint Database for BDPCA ........................................... 247
Experiments on the ORL Database for BDPCA+LDA ....................................................... 250
Experiments on the UMIST Database for BDPCA+LDA .................................................. 253
Experiments on the FERET Database for BDPCA+LDA ................................................... 254
SUMMARY ................................................................................................................................ 256
References ........................................................................................................................... 256
Chapter XIII: Feature Fusion Using Complex Discriminator .................................. 261
INTRODUCTION ....................................................................................................................... 261
SERIAL AND PARALLEL FEATURE FUSION STRATEGIES ............................................. 262
COMPLEX LINEAR PROJECTION ANALYSIS ..................................................................... 263
Fundamentals ....................................................................................................................... 263
Complex PCA ...................................................................................................................... 263
Complex LDA ..................................................................................................................... 263
FEATURE PREPROCESSING TECHNIQUES ........................................................................ 265
SYMMETRY PROPERTY OF PARALLEL FEATURE FUSION ........................................... 267
BIOMETRIC APPLICATIONS .................................................................................................. 269
Complex PCA-Based Color Image Representation ............................................................ 269
Complex LDA-Based Face Recognition ............................................................................. 271
SUMMARY ................................................................................................................................ 275
References ........................................................................................................................... 276
Index ........................................................................................................................................... 278
List of Figures ............................................................................................................ 281
List of Tables ............................................................................................................. 287
8
Preface
Personal identification and verification both play a critical role in our society. Today, more and more
business activities and work practices are computerized. E-commerce applications, such as e-banking, or
security applications, such as building access, demand fast, real-time and accurate personal
identification. Traditional knowledge-based or token-based personal identification or verification systems
are tedious, time-consuming, inefficient and expensive.
Knowledge-based approaches use "something that you know" (such as passwords and personal
identification numbers) for personal identification; token-based approaches, on the other hand, use
"something that you have" (such as passports or credit cards) for the same purpose. Tokens (e.g., credit
cards) are time-consuming and expensive to replace. Passwords (e.g., for computer login and e-mail
accounts) are hard to remember. A company may spend $14 to $28 (U.S.) on handling a password reset,
and about 19% of help-desk calls are related to the password reset problem. This may suggest that the
traditional knowledge-based password protection is unsatisfactory. Since these approaches are not
based on any inherent attribute of an individual in the identification process, they are unable to
differentiate between an authorized person and an impostor who fraudulently acquires the "token" or
"knowledge" of the authorized person. These shortcomings have led to biometrics identification or
verification systems becoming the focus of the research community in recent years.
Biometrics, which refers to automatic recognition of people based on their distinctive anatomical (e.g.,
face, fingerprint, iris, etc.) and behavioral (e.g., online/off-line signature, voice, gait, etc.) characteristics, is
a hot topic nowadays, since there is a growing need for secure transaction processing using reliable
methods. Biometrics-based authentication can overcome some of the limitations of the traditional
automatic personal identification technologies, but still, new algorithms and solutions are required.
After the Sept. 11, 2001 terrorist attacks, the interest in biometrics-based security solutions and
applications has increased dramatically, especially in the need to spot potential criminals in crowds. This
further pushes the demand for the development of different biometrics products. For example, some
airlines have implemented iris recognition technology in airplane control rooms to prevent any entry by
unauthorized persons. In 2004, all Australian international airports implemented passports using face
recognition technology for airline crews, and this eventually became available to all Australian passport
holders. A steady rise in revenues is predicted from biometrics for 2002-2007, from $928 million in 2003
to $4.035 million in 2007.
Biometrics involves the automatic identification of an individual based on his physiological or behavioral
characteristics. In a non-sophisticated way, biometrics has existed for centuries. Parts of our bodies and
aspects of our behavior have historically been used as a means of identification. The study of finger
images dates back to ancient China; we often remember and identify a person by his or her face, or by
the sound of his or her voice; and signature is the established method of authentication in banking, for
legal contracts and many other walks of life.
However, automated biometrics has only 40 years' history. As everyone knows, matching finger images
against criminal records is always an important way for law enforcers to find the criminal. But the manual
process of matching is laborious and uses too much manpower. In late 1960s, the Federal Bureau of
Investigation (FBI) began to automatically check finger images, and by the mid-1970s, a number of
automatic finger scanning systems had been installed. Among these systems, Identimat is the first
commercial system, as part of a time clock at Shearson Hamill, a Wall Street investment firm. This
system measured the shape of hand and looked particularly at finger length. Though the production of
Identimat ceased in late 1980s, its use pioneered the application of hand geometry and set a path for
biometrics technologies as a whole. Besides finger and hand, some other biometrics techniques have
also been developed. For example, fingerprint-based automatic checking systems were widely used in
law enforcement by the FBI and other U.S. government departments. Advances in hardware, such as
faster processing power and greater memory capacity, made biometrics more viable. Since the 1990s,
iris, retina, face, voice, signature, DNA and palmprint technologies have joined the biometric family.
From 1996, and especially in 1998, more funds had been given to biometrics technology research and
development. Therefore, research on biometrics became more active and exceeded the stage of
separate research dispersed in pattern recognition, signal processing, image processing, computer
vision, computer security and other subjects. By its distinguished features, such as live scan, identical
person maximum likelihood and different person minimum likelihood, biometrics grew into an independent
research field. A series of prominent events also shows that biometrics is garnering much more attention
9
in both academia and industry. For example, in September 1997, Proceedings of IEEE published a
special issue on automated biometrics; in April 1998, the BioAPI Consortium was formed to develop a
widely available and accepted API (application program interface) that will serve for various biometrics
technologies.
Today, biometrics-based authentication and identification are emerging as a reliable method in our
international and interconnected information society. With rapid progress in electronics and Internet
commerce, there has been a growing need for secure transaction processing using biometrics
technology. This means that biometrics technology is no longer only the high-tech gadgetry of Hollywood
science-fiction movies. Many biometrics systems are being used for access control, computer security
and law enforcement. The future of biometrics technology is promising. More and more biometrics
systems will be deployed for different applications in our daily life. Several governments are now, or will
soon be, using biometrics technology, such as the U.S. INSPASS immigration card or the Hong Kong ID
card, both of which store biometric features for authentication. Also, banking and credit companies have
applied biometrics technology to their business processes. In active use by some airports and airlines
even before the Sept. 11, 2001 disaster, more are seriously considering the use of bio-metric
authentication in the wake of these events. Now, biometrics technology not only protects our information
and our property, but also safeguards our lives and our society.
Automated biometrics deal with image discrimination for a fingerprint, palmprint, iris, hand or face, which
can be used to authenticate a person's claim to identity or establish an identity from a database. In other
words, image discrimination is an elementary problem in the area of automated biometrics. With the
development of biometrics and its applications, many classical discrimination technologies are borrowed
and applied to deal with biometric images. Among them, principal component analysis (PCA, or K-L
transform) and Fisher linear discriminant analysis (LDA) turns out to be very successful, in particular for
face image recognition. Also, these methods have been greatly improved with respect to the specific
biometric image analysis and applications. Recently, non-linear projection analysis technology
represented by kernel principal component analysis (KPCA) and kernel Fisher discriminant (KFD), also
show great potential in dealing with biometric problems. In fact, discrimination technologies can play an
important role in the implementation of biometric systems. They provide methodologies for automated
personal identification or verification. In turn, the applications in biometrics also facilitate the development
of discrimination methodologies and technologies, making discrimination algorithms more suitable for
image feature extraction and recognition. Since image discrimination is an elementary problem in the
area of automated biometrics, biometric image discrimination (BID) should be developed. Now, many
researchers not only apply the technology to BID, but also improve these useful approaches, even
develop many related new methods. However, according to the authors' best knowledge, so far, very few
books have been found exclusively devoted to such technology of BID.
In fact, BID technologies can be briefly defined as automated methods of feature extraction and
recognition based on given biometric images. It should be stressed that the BID technologies are not the
simple application of classical discrimination techniques to biometrics, but the improved or reformed
discrimination techniques that are more suitable (e.g., more powerful in recognition performance or
computational more efficient for feature extraction or classification) for biometrics applications. In other
words, BID technologies should be with respect to the characteristics of BID problems, and find effective
ways to solve these problems.
In general, BID problems have the following three characteristics: (1) High dimensional—This is due to
the high-dimensional characteristic of biometric images, which make the direct classification in image
space almost impossible, because the similarity calculation is computationally very expensive, as well as
the large amounts of storage is required, let alone the performance of classification in varying lighting
condition. So, a dimension reduction technique is necessary prior to recognition. (2) Large scale—In real-
world applications, there are a number of typical large-scale BID problems. Given an input biometric
sample, a large-scale BID identification system determines if the pattern is associated with any of a large
number of enrolled identities, and these large-scale BID applications require high-quality BID
technologies with good generalization power. (3) Small sample size—Differing from optical character
recognition (OCR) problems, the training samples per class are always very limited, even one sample
available for each individual, in real-world BID problems. The characteristics of high-dimensionality and
small sample size make the BID problems become the so-called small sample size problems. In these
problems, the within-class scatter matrix is always singular because the training sample size is generally
less than the space dimension.
On BID problems, above all, we should determine how to represent the biometric images. The objectives
of image representation are twofold. One is for a better identification (or verification), and the other is for
10