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Kernel Learning Algorithms for Face Recognition de Jun-Bao Li

Kernel Learning Algorithms for Face Recognition

Autor Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan
en Limba Engleză Paperback – 23 aug 2016
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.
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Specificații

ISBN-13: 9781493952120
ISBN-10: 1493952129
Pagini: 240
Ilustrații: XV, 225 p. 58 illus., 19 illus. in color.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.35 kg
Ediția:Softcover reprint of the original 1st ed. 2014
Editura: Springer
Colecția Springer
Locul publicării:New York, NY, United States

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

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