Biometric Authentication
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Biometric Authentication
A breakthrough approach to improving biometrics performanceConstructing robust information processing systems for face and voice recognitionSupporting high-performance data fusion in multimodal systemsAlgorithms, 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 matchingTheoretical pillars of machine learning for complex pattern recognition and classificationExpectation-maximization (EM) algorithms and support vector machines (SVM)Multi-layer learning models and back-propagation (BP) algorithmsProbabilistic decision-based neural networks (PDNNs) for face biometricsFlexible structural frameworks for incorporating machine learning subsystems in biometric applicationsHierarchical mixture of experts and inter-class learning strategies based on class-based modular networksMulti-cue data fusion techniques that integrate face and voice recognitionApplication case studies
Biometrics
An insight into the biometric industry and the steps for successful deployment Biometrics technologies verify identity through characteristics such as fingerprints, voices, and faces. By providing increased security and convenience, biometrics have begun to see widespread deployment in network, e-commerce, and retail applications. This book provides in-depth analysis of biometrics as a solution for authenticating employees and customers. Leading authority, Samir Nanavati explores privacy, security, accuracy, system design, user perceptions, and lessons learned in biometric deployments. He also assesses the real-world strengths and weaknesses of leading biometric technologies: finger-scan, iris-scan, facial-scan, voice-scan, and signature-scan. This accessible book is a necessary step in understanding and implementing biometrics. Demystifies the complex world of optical networks for IT and business managers Over the past few years, the cost of fiber optic networking has decreased, making it the best solution for providing virtually unlimited bandwidth for corporate LANs and WANs, metropolitan networks, Internet access, and broadband to the home. The only strategic book on optical networking technologies written from a real-world business perspective, Optical Networking demystifies complex fiber technologies for managers, and details the practical business benefits an optical network can offer. Debra Cameron explores established and emerging markets for optical networks as well as the enabling technologies, applications, network architectures, key deployment issues, and cost considerations. She also provides in-depth case studies of optical networks now in use in the United States and abroad.
Biometric User Authentication for IT Security
Author: Claus Vielhauer
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-12-28
Biometric user authentication techniques evoke an enormous interest by science, industry and society. Scientists and developers constantly pursue technology for automated determination or confirmation of the identity of subjects based on measurements of physiological or behavioral traits of humans. Biometric User Authentication for IT Security: From Fundamentals to Handwriting conveys general principals of passive (physiological traits such as fingerprint, iris, face) and active (learned and trained behavior such as voice, handwriting and gait) biometric recognition techniques to the reader. Unlike other publications in this area that concentrate on passive schemes, this professional book reflects a more comprehensive analysis of one particular active biometric technique: handwriting. Aspects that are thoroughly discussed include sensor characteristic dependency, attack scenarios, and the generation of cryptographic keys from handwriting.
