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Title Biometric authentication : a machine learning approach / S.Y. Kung, M.W. Mak, S.H. Lin.
Imprint Upper Saddle River, NJ : Prentice Hall Professional Technical Reference, ©2005.


 Internet  Electronic Book    AVAILABLE
Description 1 online resource (xv, 476 pages) : illustrations.
Series Prentice Hall information and system sciences series
Prentice-Hall information and system sciences series.
Bibliog. Includes bibliographical references (pages 427-456) and index.
Note Use copy Restrictions unspecified star MiAaHDL
Available only to authorized UTEP users.
Reproduction Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Note Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. MiAaHDL
Print version record.
digitized 2010 HathiTrust Digital Library committed to preserve pda MiAaHDL
Subject Pattern recognition systems.
Identification -- Automation.
Biometric identification.
Genre Electronic book.
Contents 1. Overview -- 2. Biometric authentication systems -- 3. Expectation-maximization theory -- 4. Support vector machines -- 5. Multi-layer neural networks -- 6. Modular and hierarchical networks -- 7. Decision-based neural networks -- 8. Biometric authentication by face recognition -- 9. Biometric authentication by voice recognition -- 10. Multicue data fusion -- App. A. Convergence properties of EM -- App. B. Average DET curves -- App. C. Matlab projects.
Summary A breakthrough approach to improving biometrics performance Constructing robust information processing systems for face and voice recognition Supporting high-performance data fusion in multimodal systems Algorithms, implementation techniques, and application examples Machine learning: driving significant improvements in biometric performance As they improve, biometric authentication systems are becoming increasingly indispensable for protecting life and property. This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. Three leading researchers bridge the gap between research, design, and deployment, introducing key algorithms as well as practical implementation techniques. They demonstrate how to construct robust information processing systems for biometric authentication in both face and voice recognition systems, and to support data fusion in multimodal systems. Coverage includes: How machine learning approaches differ from conventional template matching Theoretical pillars of machine learning for complex pattern recognition and classification Expectation-maximization (EM) algorithms and support vector machines (SVM) Multi-layer learning models and back-propagation (BP) algorithms Probabilistic decision-based neural networks (PDNNs) for face biometrics Flexible structural frameworks for incorporating machine learning subsystems in biometric applications Hierarchical mixture of experts and inter-class learning strategies based on class-based modular networks Multi-cue data fusion techniques that integrate face and voice recognition Application case studies
Other Author Mak, M. W.
Lin, Shang-Hung, 1968-
Other Title Print version: Kung, S.Y. (Sun Yuan). Biometric authentication. Upper Saddle River, NJ : Prentice Hall Professional Technical Reference, ©2005 0131478249