Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10958
Title: Optimal subset-division based discrimination and its kernelization for face and palmprint recognition
Authors: Jing, X
Li, S
Zhang, D 
Lan, C
Yang, J
Keywords: Discriminant analysis
Face recognition
Improved stability criterion
Kernel method
Optimal subset-division
Palmprint recognition
Issue Date: 2012
Publisher: Elsevier
Source: Pattern recognition, 2012, v. 45, no. 10, p. 3590-3602 How to cite?
Journal: Pattern recognition 
Abstract: Discriminant analysis is effective in extracting discriminative features and reducing dimensionality. In this paper, we propose an optimal subset-division based discrimination (OSDD) approach to enhance the classification performance of discriminant analysis technique. OSDD first divides the sample set into several subsets by using an improved stability criterion and K-means algorithm. We separately calculate the optimal discriminant vectors from each subset. Then we construct the projection transformation by combining the discriminant vectors derived from all subsets. Furthermore, we provide a nonlinear extension of OSDD, that is, the optimal subset-division based kernel discrimination (OSKD) approach. It employs the kernel K-means algorithm to divide the sample set in the kernel space and obtains the nonlinear projection transformation. The proposed approaches are applied to face and palmprint recognition, and are examined using the AR and FERET face databases and the PolyU palmprint database. The experimental results demonstrate that the proposed approaches outperform several related linear and nonlinear discriminant analysis methods.
URI: http://hdl.handle.net/10397/10958
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2012.04.001
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