Two Novel Facial Feature Extraction Methods

Two Novel Facial Feature Extraction Methods

David Zhang (Hong Kong Polytechnic University, Hong Kong), Fengxi Song (New Star Research Institute Of Applied Technology, China), Yong Xu (Harbin Institute of Technology, China) and Zhizhen Liang (Shanghai Jiao Tong University, China)
DOI: 10.4018/978-1-60566-200-8.ch006
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Abstract

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 discriminant criterion, i.e. maximum scatter difference. The second is discriminant based on coefficients of variances (DCV) which can be viewed as a generalization of N-LDA. At last, we give a summery of the chapter.
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Multiple Maximum Scatter Difference

The maximum scatter difference (MSD) discriminant criterion (Song, Zhang, Chen, & Wang, 2007) presented in Section 3.4 is a binary discriminant criterion for pattern classification. Because MSD utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, it avoids the singularity problem when addressing the SSS problems that trouble the Fisher Discriminant Criterion. Further, experimental studies demonstrated that MSD classifiers based on this discriminant criterion have been quite effective on face recognition tasks (Song et al., 2007). The drawback of the MSD classifier is that, as a binary classifier, it cannot be applied directly to multiclass classification problems such as face recognition. This means that multiple recognition tasks have to be divided into a series of binary classification problems using one of three implementation strategies: one-vs-rest, one-vs-one, or directed-acyclic-graph (Hsu & Lin, 2002). Experiments have shown that MSD classifiers are not very effective when using the first strategy, while using the latter two strategies requires the training of l(l-1)/2 MSD classifiers for a l-class recognition problem. The efficiency of such an approach will greatly be affected by any increase in the number of classes. Ultimately, then, like all binary classifiers, MSD classifiers are not suitable for large-scale pattern recognition problems.

To address the problem, this section generalizes the classification-oriented binary criterion to its multiple counterpart—multiple maximum scatter difference (MMSD) discriminant criterion for facial feature extraction (Song, Liu, & Yang, 2006; Song, Zhang, Mei, & Guo, 2007). The MMSD feature extraction method based on this novel discriminant criterion is a new subspace- based feature extraction method. Unlike most conventional subspace-based feature extraction methods that derive their discriminant vectors either in the range of the between-class scatter matrix or in the null space of the within-class scatter matrix, MMSD computes its discriminant vectors in both subspaces. MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that MMSD outperforms many state-of-the-art facial feature extraction methods including nullspace LDA (N-LDA) (Chen, Liao, Ko, Lin, & Yu, 2000), direct LDA (D-LDA) (Yu & Yang, 2001), Eigenface (Turk & Pentland, 1991), Fisherface (Belhumeur, Hespanha, & Krieqman, 1997), and complete LDA (Yang, Frangi, Yang, Zhang, & Jin, 2005).

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Table of Contents
Acknowledgment
Chapter 1
Overview  (pages 1-23)
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for... Sample PDF
Overview
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Chapter 2
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
This chapter is a brief introduction to biometric discriminant analysis technologies — Section I of the book. Section 2.1 describes two kinds of... Sample PDF
Discriminant Analysis for Biometric Recognition
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Chapter 3
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
As mentioned in Chapter II, there are two kinds of LDA approaches: classification- oriented LDA and feature extraction-oriented LDA. In most... Sample PDF
Discriminant Criteria for Pattern Classification
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Chapter 4
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
In this chapter, we first give a brief introduction to Fisher linear discriminant, Foley- Sammon discriminant, orthogonal component discriminant... Sample PDF
Orthogonal Discriminant Analysis Methods
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Chapter 5
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
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... Sample PDF
Parameterized Discriminant Analysis Methods
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Chapter 6
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
In this chapter, we introduce two novel facial feature extraction methods. The first is multiple maximum scatter difference (MMSD) which is an... Sample PDF
Two Novel Facial Feature Extraction Methods
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Chapter 7
Tensor Space  (pages 135-149)
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
In this chapter, we first give the background materials for developing tensor discrimination technologies in Section 7.1. Section 7.2 introduces... Sample PDF
Tensor Space
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Chapter 8
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
Tensor principal component analysis (PCA) is an effective method for data reconstruction and recognition. In this chapter, some variants of... Sample PDF
Tensor Principal Component Analysis
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Chapter 9
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
Linear discriminant analysis is a very effective and important method for feature extraction. In general, image matrices are often transformed into... Sample PDF
Tensor Linear Discriminant Analysis
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Chapter 10
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
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... Sample PDF
Tensor Independent Component Analysis and Tensor Non-Negative Factorization
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Chapter 11
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
In this chapter, we describe tensor-based classifiers, tensor canonical correlation analysis and tensor partial least squares, which can be used in... Sample PDF
Other Tensor Analysis and Further Direction
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Chapter 12
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single... Sample PDF
From Single Biometrics to Multi-Biometrics
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Chapter 13
Feature Level Fusion  (pages 273-304)
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
This chapter introduces the basis of feature level fusion and presents two feature level fusion examples. As the beginning, Section 13.1 provides an... Sample PDF
Feature Level Fusion
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Chapter 14
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
With this chapter we aims at describing several basic aspects of matching score level fusion. Section 14.1 provides a description of basic... Sample PDF
Matching Score Level Fusion
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Chapter 15
Decision Level Fusion  (pages 328-348)
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
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... Sample PDF
Decision Level Fusion
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Chapter 16
Book Summary  (pages 349-358)
David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang
With the title “Advanced Pattern Recognition Technologies with Applications to Biometrics” this book mainly focuses on two kinds of advanced... Sample PDF
Book Summary
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About the Authors