From Single Biometrics to Multi-Biometrics

From Single Biometrics to Multi-Biometrics

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.ch012
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Abstract

In the past decades while biometrics attracts increasing attention of researchers, people also have found that the biometric system using a single biometric trait may not satisfy the demand of some real-world applications. Diversity of biometric traits also means that they may have different performance such as accuracy and reliability. Multi-biometric applications emerging in recent years are a big progress of biometrics. They can overcome some shortcomings of the single biometric system and can perform well in improving the system performance. In this chapter we describe a number of definitions on biometrics, categories and fusion strategies of multi-biometrics as well as the performance evaluation on the biometric system. The first section of this chapter describes some concepts, motivation and justification of multi-biometrics. Section 12.2 provides some definitions and notations of biometric and multi-biometric technologies. Section 12.3 is mainly related to performance evaluation of various types of biometric systems. Section 12.4 briefly presents research and development of multi-biometrics.
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Introduction

As mentioned in previous Chapters, biometric technologies play an important role in access control and other systems that depend on secure personal authentication. The fact that biometrics may possess excellent properties such as universality (every person has biometric traits), uniqueness (generally, no two people have identical biometric traits), permanence (most biometric traits do not vary over time), collectability (biometric traits can be measured quantitatively) and good performance (biometric technologies can achieve accurate results under varied environmental circumstances) (Ross & Jain, 2004; Jain, Ross, & Prabhakar, 2004) provides a solid base for these systems. Indeed, biometric technology is a methodology to achieve fast, user-friendly authentication with high accuracy. As mentioned in Chapter 1, compared with biometric systems, traditional security systems, such as passwords or tokens-based methods, have some serious disadvantages.

Biometric technologies have many applications (Jain, Bolle, & Pankanti, 1999; Zhang & Jain (Eds.), 2006; Wayman, 2001; Bolle, Connell, Pankanti, Ratha, & Senior, 2004; Herzog & Reithiger, 2006; Jain & Ross, 2004; Jain, 2003). Biometrics can be incorporated in solutions to provide for Homeland Security including applications for improving airport security, strengthening border management control, in visas and in preventing ID theft. Biometrics can be also applied to secure electronic banking, investing and other financial transactions, enterprise-wide network security infrastructures, retail sales, law enforcement, and health and social services. Biometrics can also be integrated with other technologies such as encryption keys or smart cards to produce a hybrid security system. This way of exploiting biometrics is also called two-factor authentication, please refer to web site (http://www.answers.com/topic/two-factor-authentication). As shown in Chapter 1, biometric applications can be categorized into several categories.

Varieties of biometric traits can be individually applied for personal authentication (Zhang, Jing, & Yang, 2005; Zhang & Jain (Eds.), 2004; Zhang, 2004); however, the biometric system using a single biometric trait usually suffers from some problems such as unsatisfactory accuracy, spoof attacks, and restricted degrees of freedom (Bubeck, 2003). For example, manual workers with damaged or dirty hands may not be able to provide high-quality fingerprint images. In this case, fingerprint authentication seems not to be a good means for authenticating personal identity. For an iris identification system, the existing registration failure risk would reduce the reliability of the system. For a biometric system using speech, some factors, such as ambient noise, changes in behavioral attributes of the voice, and voice change due to aging, will affect the system’s performance. For a biometric system using face images, some challenges, such as variations in facial expression, pose and lighting, will limit the system’s performance. One’s keystroke trait and signature trait may vary to some extent and also bring side effects into the single biometric system using keystrokes or signature traits. All these examples imply that the single biometric system may not be guaranteed to provide a high accuracy.

Complete Chapter List

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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