Biometric Authentication

A Machine Learning Approach

Sun-Yuan Kung, Man-Wai Mak, Shang-Hung Lin

480 pages, parution le 04/10/2004

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Résumé

Preface

Biometrics has long been an active research field, particularly because of all the attention focused on public and private security systems in recent years. Advances in digital computers, software technologies, and embedded systems have further catalyzed increased interest in commercially available biometric application systems. Biometric authentication can be regarded as a special technical area in the field of pattern classification. Research and development on biometric authentication have focused on two separate fronts: one covering the theoretical aspect of machine learning for pattern classification and the other covering system design and deployment issues of biometric systems. This book is meant to bridge the gap between these two fronts, with a special emphasis on the promising roles of modern machine learning and neural network techniques.

To develop an effective biometric authentication system, it is vital to acquire a thorough understanding of the input feature space, then develop proper mapping of such feature space onto the expert space and eventually onto the output classification space. Unlike the conventional template matching approach, in which learning amounts to storing representative example patterns of a class, the machine learning approach adopts representative statistical models to capture the characteristics of patterns in the feature domain.
This book explores the rich synergy between various machine learning models from the perspective of biometric applications. Practically, the machine learning models can be adopted to construct a robust information processing system for biometric authentication and data fusion. It is potentially useful in a broad spectrum of application domains, including but not limited to biometric authentication.

L'auteur - Sun-Yuan Kung

Sun-Yuan Kung is a professor of electrical engineering at Princeton University. His research and teaching interests include VLSI signal processing; neural networks; digital signal, image, and video processing; and multimedia information systems. His books include VLSI Array Processors and Digital Neural Networks (Prentice Hall PTR).

L'auteur - Man-Wai Mak

Man-Wai Mak is an assistant professor at The Hong Kong Polytechnic University and chairman of the IEEE Hong Kong Section Computer Chapter. His research interests include speaker recognition, machine learning, and neural networks.

L'auteur - Shang-Hung Lin

Shang-Hung Lin is a senior architect at Nvidia, a leader in video and imaging products.

Sommaire

Overview
Biometric Authentication Systems
Expectation-Maximization Theory
Support Vector Machines
Multi-Layer Neural Networks
Modular and Hierarchical Networks
Decision-Based Neural Networks
Biometric Authentication by Face Recognition
Biometric Authentication by Voice Recognition
Multicue Data Fusion

Appendix A. Convergence Properties of EM
Appendix B. Average DET Curves
Appendix C. Matlab Projects

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Prentice Hall
Auteur(s)	Sun-Yuan Kung, Man-Wai Mak, Shang-Hung Lin
Parution	04/10/2004
Nb. de pages	480
Format	18,5 x 23,5
Couverture	Relié
Poids	476g
Intérieur	Noir et Blanc
EAN13	9780131478244
ISBN13	978-0-13-147824-4