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Journal of Electrical and Computer Engineering
Volume 2014, Article ID 919041, 7 pages
Research Article

Face Recognition Method Based on Fuzzy 2DPCA

School of Logistics, Linyi University, Linyi 276005, China

Received 5 August 2013; Revised 26 March 2014; Accepted 15 April 2014; Published 15 May 2014

Academic Editor: Bin-Da Liu

Copyright © 2014 Xiaodong Li. 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.


2DPCA, which is one of the most important face recognition methods, is relatively sensitive to substantial variations in light direction, face pose, and facial expression. In order to improve the recognition performance of the traditional 2DPCA, a new 2DPCA algorithm based on the fuzzy theory is proposed in this paper, namely, the fuzzy 2DPCA (F2DPCA). In this method, applying fuzzy K-nearest neighbor (FKNN), the membership degree matrix of the training samples is calculated, which is used to get the fuzzy means of each class. The average of fuzzy means is then incorporated into the definition of the general scatter matrix with anticipation that it can improve classification result. The comprehensive experiments on the ORL, the YALE, and the FERET face database show that the proposed method can improve the classification rates and reduce the sensitivity to variations between face images caused by changes in illumination, face expression, and face pose.