Overview

Overview

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.ch001
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Abstract

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics, to enhance the recognition performance of biometric systems. In Section 1.1, we discuss the necessity, importance, and applications of biometric recognition technology. A brief introduction of main biometric recognition technologies are presented in Section 1.2. In Section 1.3, we describe two advanced biometric recognition technologies, biometric data discrimination and multi-biometric technologies. Section 1.4 outlines the history of related work and highlights the content of each chapter of this book.
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Introduction

Reliable personal recognition techniques play a critical role in our everyday and social activities. In access control, authorized users should be allowed for entrance with high accuracy while unauthorized users should be denied. In welfare benefit disbursement, people not only should verify whether the identity of a person is whom he/she claimed to be, but also should avoid the occurrence that one person claims to be another person to receive the welfare benefit twice (double dipping).

Traditionally, there are two categories of personal recognition approaches, token-based and knowledge-based (Miller, 1994). In the token-based approach, the identity of a person is verified according to what he/she has. Anyone possessed a certain physical object (token), e.g., keys or ID cards, is authorized to receive the associated service. The knowledge-based approaches authenticate the identity of an individual according to what he/she knows. Any individuals with certain secret knowledge, such as passwords and answers to questions, would receive the associated service. Both the token-based and the knowledge-based approaches, however, have some inherent limitations. In the token-based approach, the “token” could be stolen or lost. In the knowledge-based approach, the “secret knowledge” could be guessed, forgotten, or shared.

Biometric recognition is an emerging personal recognition technology developed to overcome the inherent limitations of the traditional personal recognition approaches (Jain, Bolle, & Pankanti, 1999a; Zhang, 2000, 2002, & 2004; Wayman, 2005; Bolle, 2004). The term biometrics, which comes from the Greek words bios (life) and metrikos (measure), refers to a number of technologies to authenticate persons using their physical traits such as fingerprints, iris, retina, speech, face and palm print or behavior traits such as gait, handwritten signature and keystrokes. In other words, biometric recognition recognizes the identity of an individual according to who he/she is. Compared with the token-based and the knowledge-based methods, biometric identifiers cannot be easily forged, shared, forgotten, or lost, and thus can provide better security, higher efficiency, and increased user convenience.

Biometric recognition lays the foundation for an extensive array of highly secure authentication and reliable personal verification (or identification) solutions. The first commercial biometric system, Identimat, was developed in 1970s, as part of an employee time clock at Shearson Hamill, a Wall Street investment firm (Miller, 1994). It measured the shape of the hand and the lengths of the fingers. At the same time, fingerprint-based automatic personal authentication systems were widely used in law enforcement by the FBI and by US government departments. Subsequently, advances in hardware such as faster processing power and greater memory capacity made biometrics more feasible and effective. Since the 1990s, iris, retina, face, voice, palm print, signature and DNA technologies have joined the biometric family (Jain et al., 1999a; Zhang, 2000).

With the increasing demand for reliable and automatic solutions to security systems, biometric recognition is becoming ever more widely deployed in many commercial, government, and forensic applications. After the 911 terrorist attacks, the interest in biometrics-based security solutions and applications increased dramatically, especially in the need to identify individuals in crowds. Some airlines have implemented iris recognition technology in airplane control rooms to prevent any entry by unauthorized persons. In 2004, all Australian international airports implemented passports using face recognition technology for airline crews and this will eventually became available to all Australian passport holders (Jain et al., 1999a). Several governments are now using or will soon be using biometric recognition technology. The U.S. INSPASS immigration card and the Hong Kong ID card, for example, both store biometric features for reliable and convenient personal authentication.

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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