2D Image Matrix-Based Discriminator

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

Discriminant DCT Feature Extraction

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 first. Then, we describe the presented discriminant DCT approach and analyze its theoretical properties. Finally, we offer detailed experimental results and a chapter summary.

Complete Kernal Fisher Discrimination Analysis

This chapter introduces a complete kernel Fisher discriminant analysis (KFD) that is a useful statistical technique applied to biometric application. We first describe theoretical perspective of KPCA. Then, a new KFD algorithm framework, KPCA plus LDA, is given. Afterwards, we discuss the complete KFD algorithm. Finally, the experimental results and chapter summary are given.

Solutions of LDA for Small Sample Size Problems

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 for LDA and a combined LDA algorithm for SSS problem are described. Finally, we provide the experimental results and some conclusions.

Feature Fusion Using Complex Descriminator

This chapter describes feature fusion techniques using complex discriminator. After the introduction, we first introduce serial and parallel feature fusion strategies. Then, the complex linear projection analysis methods, complex PCA and complex LDA, are developed. Next, some feature preprocessing techniques are given. The symmetry property of parallel feature fusion is analyzed and revealed. Then, the proposed methods are applied to biometric applications, related experiments are performed and the detailed comparison analysis is exhibited. Finally, a summary is given.

Statistical Uncorrelation Analysis

This chapter shows a special LDA approach called optimal discrimination vectors (ODV), which requires that every discrimination vector satisfy the Fisher criterion. After introduction, we first give some basic definitions. Then, uncorrelated optimal discrimination vectors (UODV) are proposed. Next, we introduce an improved UODV approach, and offer some experiments and analysis. Finally, we summarize some useful conclusions.

An Introduction to Biometrics Image Discrimination (BID)

In this chapter, we briefly introduce biometrics image discrimination (BID) technologies. First, we define and describe types of biometrics and biometrics technologies. Then, some applications of biometrics are given. The next section discusses biometrics systems and discrimination technologies, followed by a definition of BID technologies. The history and development of BID technologies is offered, and an overview and taxonomy of appearance-based BID technologies, respectively, is provided. Finally, the last section highlights each chapter of this book.

Two-Directional PCA/LDA

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 (BDPCA) and BDPCA plus LDA. Then, some basic models and definitions related to two-directional PCA/LDA approach are given. Next, we discuss two-directional PCA plus LDA. And, finally, the experimental results and chapter summary are given.

Principle Component Analysis

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, we introduce PCA definitions and related technologies. Following, we discuss non-linear PCA technologies. Finally, some useful conclusions are summarized.

Other Typical BID Improvements

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. After the introduction, we describe DEM. Then, post-processing on LDA-based method is defined. Finally, we offer some brief conclusions.