Membership-Degree Preserving Discriminant Analysis with Applications to Face RecognitionReport as inadecuate

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Computational and Mathematical Methods in MedicineVolume 2013 2013, Article ID 275317, 7 pages

Research ArticleSchool of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received 18 August 2013; Accepted 14 September 2013

Academic Editor: Qingshan Liu

Copyright © 2013 Zhangjing Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -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.

Author: Zhangjing Yang, Chuancai Liu, Pu Huang, and Jianjun Qian



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