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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Book Description Prentice Hall, 2004. Hardcover. Book Condition: New. Bookseller Inventory # DADAX0131478249
Book Description Prentice Hall, 2004. Hardcover. Book Condition: New. New item. Bookseller Inventory # QX-133-94-1559101