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A novel kernel discriminant feature extraction framework based on mapped virtual samples for face recognition

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5 Author(s)
Sheng Li ; Coll. of Autom., Nanjing Univ. of Posts & Telecommun., Nanjing, China ; Xiaoyuan Jing ; Zhang, D. ; Yongfang Yao
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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.

Published in:

Image Processing (ICIP), 2011 18th IEEE International Conference on

Date of Conference:

11-14 Sept. 2011