David Zhang

With the increasing concerns on security breaches and transaction fraud, highly reliable and convenient personal verification and identification technologies are more and more...

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric...

This chapter is a brief introduction to biometric discriminant analysis technologies — Section I of the book. Section 2.1 describes two kinds of linear discriminant analysis...

As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most chapters of this session of the book...

In this chapter, we first give a brief introduction to Fisher linear discriminant, Foley- Sammon discriminant, orthogonal component discriminant, and application strategies for...

In this chapter, we mainly present three kinds of weighted LDA methods. In Sections 5.1, 5.2 and 5.3, we respectively present parameterized direct linear discriminant analysis...

In this chapter, we introduce two novel facial feature extraction methods. The first is multiple maximum scatter difference (MMSD) which is an extension of a binary linear...

In this chapter, we first give the background materials for developing tensor discrimination technologies in Section 7.1. Section 7.2 introduces some basic notations in tensor...

Tensor principal component analysis (PCA) is an effective method for data reconstruction and recognition. In this chapter, some variants of classical PCA are introduced and the...

Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into vectors prior to feature...

In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric...

In this chapter, we describe tensor-based classifiers, tensor canonical correlation analysis and tensor partial least squares, which can be used in biometrics. Section 11.1 gives...

In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single biometric trait may not satisfy...

This chapter introduces the basis of feature level fusion and presents two feature level fusion examples. As the beginning, Section 13.1 provides an introduction to feature level...

With this chapter we aims at describing several basic aspects of matching score level fusion. Section 14.1 provides a description of basic characteristics of matching score...

With this chapter, we first present a variety of decision level fusion rules and classifier selection approaches, and then show a case study of face recognition based on decision...

With the title “Advanced Pattern Recognition Technologies with Applications to Biometrics” this book mainly focuses on two kinds of advanced biometric recognition technologies...

Biometric Image Discrimination Technologies addresses highly relevant issues to many fundamental concerns of both researchers and practitioners of biometric image discrimination...

In this chapter, we briefly introduce biometrics image discrimination (BID) technologies. First, we define and describe types of biometrics and biometrics technologies. Then...

In this chapter, we first describe some basic concepts of PCA, a useful statistical technique that can be used in many fields, such as face patterns and other biometrics. Then...

This chapter deals with issues related to linear discriminant analysis (LDA). In the introduction, we indicate some basic conceptions of LDA. Then, the definitions and notations...

In this chapter, we show some PCA/LDA applications in biometrics. Based on the introductions to both PCA and LDA mentioned in Chapters II and III, their simple descriptions are...

This chapter shows a special LDA approach called optimal discrimination vectors (ODV), which requires that every discrimination vector satisfy the Fisher criterion. After...

This chapter shows the solutions of LDA for small sample-size (SSS) problems. We first give an overview on the existing LDA regularization techniques. Then, a unified framework...

This chapter gives an improved LDA (ILDA) approach. After a short review and comparison of major linear discrimination methods, including the eigenface method, fisherface method...

This chapter provides a feature extraction approach that combines the discrete cosine transform (DCT) with LDA. The DCT-based frequency-domain analysis technique is introduced...

In this chapter, we discuss some other typical BID improvements, including dual eigenspaces method (DEM) and post-processing on LDA-based method for automated face recognition....

This chapter introduces a complete kernel Fisher discriminant analysis (KFD) that is a useful statistical technique applied to biometric application. We first describe...

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image...

This chapter introduces a two-directional PCA/LDA approach that is a useful statistical technique applied to biometric authentication. We first describe both bi-directional PCA...

This chapter describes feature fusion techniques using complex discriminator. After the introduction, we first introduce serial and parallel feature fusion strategies. Then, the...