Letters: Feature extraction using fuzzy inverse FDA

Authors:
Wankou Yang;Jianguo Wang;Mingwu Ren;Lei Zhang;Jingyu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China and Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China and Tangshan College, Network and Education Center, Tangshan 063000, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper proposes a new method of feature extraction and recognition, namely, the fuzzy inverse Fisher discriminant analysis (FIFDA) based on the inverse Fisher discriminant criterion and fuzzy set theory. In the proposed method, a membership degree matrix is calculated using FKNN, then the membership degree is incorporated into the definition of the between-class scatter matrix and within-class scatter matrix to get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. Experimental results on the ORL, FERET face databases and pulse signal database show that the new method outperforms Fisherface, fuzzy Fisherface and inverse Fisher discriminant analysis.