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One of the challenges for computational intelligence and biometrics is to understand how people process and recognize faces and to develop automated and reliable face recognition systems. Biometrics has become the major component in the complex decision making process associated with security applications. The many challenges addressed for face detection and authentication include cluttered environments, occlusion and disguise, temporal changes, robust training and open set testing.
Reliable Face Recognition Methods seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor such as neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. This book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development.
Endorsements by: Ruud Bolle (IBM), John Daugman (Cambridge University, UK), David Zhang (Hong Kong Polytechnic University, China), Stan Li (Chinese Academy of Sciences, China), Tom Huang (University of Illinois, USA).
List of contents
The Human Face.- Modeling and Prediction.- Data Collection.- Face Representation.- Face Recognition.- Face in a Crowd.- 3D.- Data Fusion.- Denial and Deception.- Augmented Cognition.- Performance Evaluation.- Error Analysis.- Security and Privacy.- e-Science and Computing.- Epilogue.
Summary
One of the challenges for computational intelligence and biometrics is to understand how people process and recognize faces and to develop automated and reliable face recognition systems. Biometrics has become the major component in the complex decision making process associated with security applications. The many challenges addressed for face detection and authentication include cluttered environments, occlusion and disguise, temporal changes, robust training and open set testing.
Reliable Face Recognition Methods seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor such as neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. This book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development.
Endorsements by: Ruud Bolle (IBM), John Daugman (Cambridge University, UK), David Zhang (Hong Kong Polytechnic University, China), Stan Li (Chinese Academy of Sciences, China), Tom Huang (University of Illinois, USA).
