Biometric Authentication: A Machine Learning Approach (paperback) (Prentice Hall Information and System Sciences Series) - Tapa blanda

Acerca del autor

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

De la contraportada

• 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