Feature Level Fusion

Feature Level Fusion

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.ch013
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Abstract

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 fusion. Section 13.2 describes two classes of feature level fusion schemes. Section 13.3 gives a feature level fusion example that fuses face and palm print. Section 13.4 presents a feature level fusion example that fuses multiple feature presentations of a single palm print trait. Finally, Section 13.5 offers brief comments.
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Introduction

Fusion at the feature level means that the combination of different biometric traits occurs at an early stage of the multi-biometric system (Chang, Bowyer, & Sarkar, 2003; Gunatilaka & Baertlein, 2001; Gunes & Piccardi, 2005; Jain, Ross, & Prabhakar, 2004; Kober, Harz, & Schiffers, 1997; Kong, Zhang, & Kamel, 2006; Ross & Govindarajan, 2005; Ross & Jain, 2003). The traits fused at the feature level will be used in the matching and decision-making modules of the multi-biometric system to obtain authentication results. As pointed out by Jain, Ross and Prabhakar (2004) and other researchers (Choi, Choi, & Kim, 2005; Ratha, Connell, & Bolle, 1998; Singh, Vatsa, Ross, & Noore, 2005), it is likely that the integration at the feature level is able to produce a higher accuracy than fusion at the matching score level and fusion at the decision level. This is because the feature representation conveys richer information than the matching score or the verification decision (i.e. accept or reject) of a biometric trait. In contrast, the decision level is so coarse that much information loss will be caused (Sim, Zhang, Janakiraman, & Kumar, 2007). Following are some examples of fusion at the feature level in the field of biometrics. Chang, Bowyer and Sarkar (2003) fused appearance traits of the face and ear at the feature level. They concatenated the traits of the face and ear and exploited the resultant new one-dimensional data to perform personal authentication. To reduce the dimensionality of the new data, they extracted features from the new data. Ross and Govindarajan (2005) discussed fusion at the feature level in the following different scenarios such as fusion of PCA and LDA coefficients of face images and fusion of face and hand modalities. Kong, Zhang and Kamel (2006) fused the phase information of different Gabor filtering results at the feature level to perform palm print identification. Gunatilaka and Baertlein (2001) proposed a feature-level fusion approach for fusing data generated from non-coincidently sampled sensors. Other feature level fusion examples include fusion of visual and acoustic signals (Kober, Harz, & Schiffers, 1997), the fusion of face and body information (Gunes & Piccardi, 2005), fusion of face and fingerprint (Rattani, Kisku, Bicego, & Tistarelli, 2006,2007), fusion of side face and gait (Zhou & Bhanu, 2008), fusion of iris and face and (Son & Lee, 2005), fusion of palm print and palm vein (Wang, Yau, Suwandy, & Sung, 2008), fusion of lip and audio (Chetty & Wagner, 2008), etc. Feature level fusion is also implemented for other fields such as medical image fusion (Kor & Tiwary, 2004; Patnaik, 2006) object classification (Wender & Dietmayer, 2007), machinery fault diagnosis (Liu, Ma, & Mathew, 2006) and content-based image retrieval (Rahman, Desai, & Bhattacharya, 2006).

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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