This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

Fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems Provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications Describes a broad range of applications Includes supplementary material: sn.pub/extras

Autorentext
Dr. Stan Z. Li is Professor at the National Laboratory of Pattern Recognition, Director of the Center for Biometrics and Security Research, and Director of the R&D Center for Visual Internet of Things, within the Chinese Academy of Sciences. Dr. Anil K. Jain is University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University, U.S.A.

Klappentext

The history of computer-aided face recognition dates back to the 1960s, yet the problem of automatic face recognition a task that humans perform routinely and effortlessly in our daily lives still poses great challenges, especially in unconstrained conditions.

This highly anticipated new edition of the Handbook of Face Recognition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following 26 chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions.

Topics and features:

• Fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems
• Examines the design of accurate, reliable, and secure face recognition systems
• Provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications
• Contains numerous step-by-step algorithms
• Describes a broad range of applications from person verification, surveillance, and security, to entertainment
• Presents contributions from an international selection of preeminent experts
• Integrates numerous supporting graphs, tables, charts, and performance data This practical and authoritative reference is the essential resource for researchers, professionals and students involved in image processing, computer vision, biometrics, security, Internet, mobile devices, human-computer interface, E-services, computer graphics and animation, and the computer game industry.

Dr. Stan Z. Li is Professor at the National Laboratory of Pattern Recognition, Director of the Center for Biometrics and Security Research, and Director of the R&D Center for Visual Internet of Things, within the Chinese Academy of Sciences. Dr. Anil K. Jain is University Distinguished Professor in the Department of Computer Science and Engineering at Michigan State University, U.S.A.

Inhalt

Introduction.- Face Recognition in Subspaces.- Face Subspace Learning.- Local Representation of Facial Features.- Face Alignment Models.- Morphable Models of Faces.- Illumination Modeling for Face Recognition.- Face Recognition Across Pose and Illumination.- Skin Color in Face Analysis.- Face Aging Modeling.- Face Detection.- Facial Landmark Localization.- Face Tracking and Recognition in Video.- Face Recognition at a Distance.- Face Recognition Using Near Infrared Images.- Multispectral Face Imaging and Analysis.- Face Recognition Using 3D Images.- Facial Action Tracking.- Facial Expression Recognition.- Face Synthesis.- Evaluation Methods in Face Recognition.- Dynamic Aspects of Face Processing in Humans.- Face Recognition by Humans and Machines.- Face Recognition Applications.- Large Scale Database Search.- Face Recognition in Forensic Science.- Privacy Protection and Face Recognition.