Zhang D., Song F., Xu Y., Liang Z. Advanced Pattern Recognition Technologies with Applications to Biometrics

Издательство IGI Global, 2009, -385 pp.With the increasing concerns on security breaches and transaction fraud, highly reliable and convenient personal verification and identification technologies are more and more requisite in our social activities and national services. Biometrics, which use the distinctive physiological and behavioural characteristics to recognize the identity of an individual, are gaining ever-growing popularity in an extensive array of governmental, military, forensic, and commercial security applications.
The beginning of biometrics can be traced back to centuries ago, from when fingerprint has been used for forensics. Automated biometrics, however, has only 40 years of history. In the early 1960s, the FBI (Federal Bureau of Investigation) began to put more effort in developing automated fingerprint acquisition and identification systems. With the advances in hardware, sensor, pattern recognition, signal and image processing technologies, a number of biometric technologies, such as face, iris, retina, voice, signature, hand geometry, keystroke, ear, and palm print recognition, have been developed, and novel biometrics, such as dental, odor, and skin reflectance, have also been investigated to overcome some of the limitations of current biometric recognition technologies.
Historically, the development of biometric technologies is originated from different disciplines. For example, the beginning of fingerprint recognition research is an interaction of forensics and pattern recognition. Voice recognition technology, on the contrary, came from signal processing, and face recognition started from computer vision. This multi-discipline characteristic, however, makes it very challenging to establish an infrastructural theory framework for developing biometric recognition technologies.
Generally, a biometric system can be regarded as a pattern recognition system, where a feature set is first extracted from the acquired data, and then compared with the stored template set to make a decision on the identity of an individual. A biometric system can be applied to two fields, verification and identification. In verification mode, the decision is whether a person is who he claims to be? In identification mode, the decision is whose biometric data is this? A biometric system is thus formalized into a two-class or multi-class pattern recognition system.
A biometric system usually includes four major modules: data acquisition, feature extraction, matching, and system database, where feature extraction and matching are two of the most challenging problems in biometric recognition research, and have attracted researchers from different backgrounds: biometrics, computer vision, pattern recognition, signal processing, and neural networks. In this book, we focus on two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics. Biometric data discrimination technology, which extracts a set of discriminant features by using classical or improved discriminant analysis approaches, is of course one kind of advanced pattern recognition technology. Multi-biometrics, which integrates information from multiple biometric traits to enhance the performance and reliability of the biometric system, is another kind of advanced pattern recognition technology.
The book begins with the topic of biometric data discrimination technologies. Discriminant analysis, which aims at dimensionality reduction while retaining the statistical separation property between distinct classes, is a natural choice for biometric feature extraction. From the late 1980s, many classical discriminant analysis technologies are borrowed and applied to deal with biometric data or features. Among them, principal component analysis (PCA, or K-L transform) and Fisher linear discriminant analysis (LDA) turns out to be effective, in particular for face representation and recognition. Other linear approaches, such as independent component analysis (ICA), canonical correlation analysis (CCA), and partial least squares (PLS), have been investigated and applied to biometric recognition. Recently, non-linear projection analysis technology represented by kernel principal component analysis (KPCA), kernel Fisher discriminant (KFD), and manifold learning, also show great potential in dealing with biometric recognition problems.Overview
Section I Biometric Discriminant Analysis
Discriminant Analysis for Biometric Recognition
Discriminant Criteria for Pattern Classification
Orthogonal Discriminant Analysis Methods
Parameterized Discriminant Analysis Methods
Two Novel Facial Feature Extraction Methods
Section II Tensor Technology
Tensor Space
Tensor Principal Component Analysis
Tensor Linear Discriminant Analysis
Tensor Independent Component Analysis and Tensor Non-Negative Factorization
Other Tensor Analysis and Further Direction
Section III Biometric Fusion
From Single Biometrics to Multi-Biometrics
Feature Level Fusion
Matching Score Level Fusion
Decision Level Fusion
Book Summary