Biometric Authentication: A Machine Learning Approach (paperback) (Prentice Hall Information and System Sciences Series)
| Author | : | |
| Rating | : | 4.65 (868 Votes) |
| Asin | : | 0137074832 |
| Format Type | : | paperback |
| Number of Pages | : | 496 Pages |
| Publish Date | : | 2013-06-06 |
| Language | : | English |
DESCRIPTION:
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. This book introduces powerful machine learning tech
calvinnme said Too much information, not enough detail. Any time you can pick up a used copy of a recently published technical book on an interesting topic at one-fourth of the retail price, you know there must be a problem. You would be right. This book tries to do three things at the same time and fails with at least two of its goals. It tries to talk about the business issues of bi
This book introduces powerful machine learning techniques that significantly improve biometric performance in a broad spectrum of application domains. 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
His research and teaching interests include VLSI signal processing; neural networks; digital signal, image, and video processing; and multimedia information systems. His research interests include speaker recognition, machine learning, and neural networks. Shang-Hung Lin is a senior architect at Nvidia, a leader in video and imaging products.. Sun-Yuan Kung is a professor of electrical engineering at Princ