Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30863
Title: Feature extraction based on fuzzy 2DLDA
Authors: Yang, W
Yan, X
Zhang, L 
Sun, C
Keywords: 2DLDA
Face recognition
Feature extraction
Fisher
Fuzzy
LDA
Issue Date: 2010
Publisher: Elsevier
Source: Neurocomputing, 2010, v. 73, no. 10-12, p. 1556-1561 How to cite?
Journal: Neurocomputing 
Abstract: In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.
URI: http://hdl.handle.net/10397/30863
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2009.12.025
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