Decision Level Fusion

Decision 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.ch015
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Abstract

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 level fusion, and finally offer a summary of three levels of biometric fusion technologies. In a multi-biometric system, classifier selection techniques may be associated with the decision level fusion as follows: classifier selection is first carried out to select a number of classifiers from all classifier candidates. Then the selected classifiers make their own decisions and the decision level fusion rule is used to integrate the multiple decisions to produce the final decision. As a result, in this chapter, we also introduce classifier selection by showing a classifier selection approach based on correlation analysis. This chapter is organized as follows. Section 15.1 provides an introduction to decision level fusion. Section 15.2 presents several simple and popular decision level fusion rules such as the AND, OR, RANDOM, Voting rules, as well as the weighted majority decision rule. Section 15.3 introduces a classifier selection approach based on correlations between classifiers. Section 15.4 presents a case study of group decision-based face recognition. Finally, Section 15.5 offers some comments on three levels of biometric fusion.
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Introduction

Though the term ‘decision level fusion’ has appeared widely in the biometric literature, it is not used only in the field of biometrics. Indeed, as an information fusion strategy, decision level fusion also has been widely applied in a number of areas such as multisensor data fusion (Hall & Llinas, 1997), multispectral image fusion and geoscience data fusion (Jeon & Landgrebe, 1999; Fauvel, Chanussot, & Benediktsson, 2006). We would like to regard ‘decision level fusion’ as a term of information science rather than a term of biometrics. In some cases on multi-biometrics, the term ‘symbol level fusion’ (Tien, 2003; Gee & Abidi, 2000; Dop, 1999) is also used to represent decision level fusion. The decision level fusion strategy integrates biometric information in a simple and straightforward way in comparison with feature level fusion, which usually directly integrates different biometric traits at the feature level, and matching score level fusion which usually requires that before fusion the matching scores of different biometric subsystems be normalized. A system using the decision level fusion strategy integrates different biometric data at a later stage than the multi-biometric system using feature level fusion or matching score level fusion strategies. The multi-biometric system using the decision level fusion strategy can be described as follows: The system consists of a number of biometric subsystems each of which uses a biometric trait and makes the authentication decision independently. The decision level fusion strategy is then used to combine the decisions of the biometric subsystems to produce the final decision.

Various decision level fusion methods such as Boolean conjunctions, weighted decision methods, classical inference, Bayesian inference, and Dempster–Shafer method (Jain, Lin, Pankanti, & Bolle, 1997), voting (Zuev & Ivanon, 1996) have been proposed. Prabhakar and Jain (2002) combined classifier selection and decision level fusion techniques to perform fingerprint verification. Hong and Jain (1998) integrated faces and fingerprints at the decision level. Chatzis, Bors, and Pitas (1999) used fuzzy clustering algorithms to implement decision level fusion. Osadciw, Varshney and Veeramachaneni (2003) proposed a Bayesian framework to perform decision fusion based on multiple biometric sensors. In addition, modified KNN approach (Teoh, Samad, & Hussain, 2002), decision trees and logistic regression (Verlinde & Cholet, 1999) were also used to fuse multiple biometric traits at the decision level. More studies on decision level fusion such as fusion of iris and face, fusion of 3D data can be found in (Wang, Tan, & Jain, 2003; Gökberk & Akarun, 2006; Li, Zhao, Ao, & Lei, 2005; Gokberk, Salah, & Akarun, 2005; Freedman, 1994; Teoh, Samad, & Hussain, 2004; Niu, Han, Yang, & Tan, 2007). It should be noted that the theoretical framework described by Kittler, Hatef, Duin and Matas (1998) is able to derive a number of real rules for combining classifiers. Roli, Kittler, Fumera, and Muntoni (2002) classified the decision fusion strategies into two main classes: fixed and trained rules. Fusion strategies such as majority voting and the sum rule are recognized as fixed rules. These strategies might allow combination of different systems with similar performance to perform well. Some techniques such as weighted averaging and behavior knowledge space are examples of trained rules, which may allow combination of systems with different performance to improve authentication performance.

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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