Matching Score Level Fusion

Matching Score 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.ch014
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Abstract

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 fusion in the form of introduction. Section 14.2 shows a number of matching score fusion rules. Section 14.3 surveys several typical normalization procedures of raw matching scores. Section 14.4 gives an example of matching score level fusion method. Finally, Section 14.5 provides several brief comments on matching score fusion.
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Introduction

Matching score level fusion is the most commonly used biometric information fusion strategy because matching scores are easily available and because they retain sufficient information to distinguish genuine matching from impostor matching. Generally, a multi-biometric system based on the matching score level fusion works as follows: each subsystem of the multi-biometric system exploits one biometric trait to produce a matching score. Then these matching scores are normalized and integrated to obtain the final matching score or final decision for personal authentication. Studies on the basis and potential of score level fusion are very helpful for us to understand and implement fusion at the score level. For example, based on the likelihood ratio test Nandakumar, Chen, Dass and Jain (2008) proposed a framework for the optimal combination of matching scores. Their study showed that when finite Gaussian mixture model was used to model the distributions of genuine and impostor match scores, the matching score level fusion could produce good performance. Toh Kim, and Lee (2008b) studied the issue of optimizing the ROC performance of the multimodal biometric system using the matching score fusion strategy. Jain, Nandakumar, and Ross (2005) provided us with comprehensive descriptions of various score normalization rules. Snelick, Uludag, Mink, Indovina and Jain (2005) and Poh and Bengio (2006) presented the performances of matching score level fusion algorithms and systems obtained using elaborate evaluation.

A number of studies (Dass, Nandakumar, & Jain, 2005; Kittler, Hatef, Duin, & Matas, 1998; Poh & Bengio, 2006; Ross & Jain, 2003; Schmid, Ketkar, Singh, & Cukic, 2006; Snelick Uludag, Mink, Indovina, & Jain, 2005; Vielhauer & Scheidat, 2005) have demonstrated that the matching score level fusion strategy can lead to a higher accuracy than the single biometric system. A variety of cases of matching score fusion have also been proposed (Brunelli & Falavigna, 1995; Doddington, Liggett, Martin, Przybocki, & Reynolds, 1998; Islam, Mangayyagari, & Sankar, 2007; Kumar & Zhang, 2004; Scheidat, Vielhauer, & Dittmann, 2005; Tsalakanidou, Malassiotis, & Strintzis, 2007; Tulyakov & Govindaraju, 2005; Yan & Bowyer, 2005). For example, Ribaric and Fratric (2005) acquired images containing both fingerprints and palm prints and then used the extracted eigenpalm and eigenfinger features to perform matching score level fusion. By conducting experiments on a population approaching 1,000 individuals, Snelick, Uludag, Mink, Indovina and Jain (2005) demonstrated that the multimodal fingerprint and face biometric system, which combines the two biometric traits at the matching score level, was significantly more accurate than any individual biometric systems.

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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