Discusses the system framework of kernel based face recognition
Introduces a new method of machine learning as it relates to face recognition
Presents algorithms for pattern recognition and machine learning
Jeng-Shyang Pan is the Tainan Chapter Chair, IEEE Signal Processing Society.
Jun-Bao Li is currently at the Department of Automatic Test and Control, Harbin Institute of Technology.
Shu-Chuan Chu is a researcher at the School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus.
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
Introduction.- Statistical Learning and Face Recognition.- Kernel Learning Foundation.- Kernel Principal Analysis Based Face Recognition.- Kernel Discriminant Analysis Based Face Recognition.- Kernel Manifold Learning Based Face Recognition.- Kernel Semi-supervised Based Face Recognition.- Kernel Learning Based Face Recognition for Smart Environment.- Kernel Optimization Based Face Recognition.- Kernel Construction for Face Recognition.
This book discusses the advanced kernel learning algorithms and its application on face recognition. The book focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. This authors aim to solve the parameter selection problems endured by kernel learning algorithms, and presents kernel optimization method with the data dependent kernel. This text extends the definition of data-dependent kernel and applies it to kernel optimization. Included within are algorithms of kernel based face recognition and the feasibility of the kernel based face recognition method.
This book covers the framework of kernel based face recognition. It discusses the advanced kernel learning algorithms and its application on face recognition. The book also focuses on the theoretical deviation, the system framework and experiments.
Introduction.- Statistical Learning and Face Recognition.- Kernel Learning Foundation.- Kernel Principal Analysis Based Face Recognition.- Kernel Discriminant Analysis Based Face Recognition.- Kernel Manifold Learning Based Face Recognition.- Kernel Semi-supervised Based Face Recognition.- Kernel Learning Based Face Recognition for Smart Environment.- Kernel Optimization Based Face Recognition.- Kernel Construction for Face Recognition.
Jeng-Shyang Pan is the Tainan Chapter Chair, IEEE Signal Processing Society.
Jun-Bao Li is currently at the Department of Automatic Test and Control, Harbin Institute of Technology.
Shu-Chuan Chu is a researcher at the School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus.
Über den Autor
Jeng-Shyang Pan is the Tainan Chapter Chair, IEEE Signal Processing Society.
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Jun-Bao Li is currently at the Department of Automatic Test and Control, Harbin Institute of Technology.
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Shu-Chuan Chu is a researcher at the School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus.
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Inhaltsverzeichnis
Introduction.- Statistical Learning and Face Recognition.- Kernel Learning Foundation.- Kernel Principal Analysis Based Face Recognition.- Kernel Discriminant Analysis Based Face Recognition.- Kernel Manifold Learning Based Face Recognition.- Kernel Semi-supervised Based Face Recognition.- Kernel Learning Based Face Recognition for Smart Environment.- Kernel Optimization Based Face Recognition.- Kernel Construction for Face Recognition.
Klappentext
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
Discusses the system framework of kernel based face recognition
Introduces a new method of machine learning as it relates to face recognition
Presents algorithms for pattern recognition and machine learning
