membership-degree preserving discriminant analysis with applications to face recognition

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ID: 249467
2013
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Abstract
In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.
Reference Key
yang2013computationalmembership-degree Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhangjing Yang;Chuancai Liu;Pu Huang;Jianjun Qian
Journal advanced functional materials
Year 2013
DOI
10.1155/2013/275317
URL
http://dx.doi.org/10.1155/2013/275317
Keywords
medical informatics computer applications to medicine

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