Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8982
Title: A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition
Authors: Li, S
Jing, X
Zhang, D 
Yao, Y
Bian, L
Keywords: Face recognition
Kernel discriminant feature extraction framework
Mapped virtual samples (MVS)
MVS-based kernel discriminant approaches
Issue Date: 2011
Source: Proceedings - International Conference on Image Processing, ICIP, 2011, p. 3005-3008 How to cite?
Abstract: In this paper, we propose a novel kernel discriminant feature extraction framework based on the mapped virtual samples (MVS) for face recognition. We calculate a non-symmetric kernel matrix by constructing a few virtual samples (including eigen-samples and common vector samples) in the input space, and then express kernel projection vectors by using mapped virtual samples (MVS). Under this framework, we realize two MVS-based representative kernel methods including kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA). Experimental results on the AR and CAS-PEAL face databases demonstrate that the proposed framework can effectively improve the classification performance of kernel discriminant methods. In addition, the MVS-based kernel approaches have a lower computational cost in contrast with the related kernel methods.
Description: 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, 11-14 September 2011
URI: http://hdl.handle.net/10397/8982
ISBN: 9781457713033
ISSN: 1522-4880
DOI: 10.1109/ICIP.2011.6116295
Appears in Collections:Conference Paper

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