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Newly Regularized LDA for SSS Problem with application to Face Recognition
곽려혜(Lee Hui Kueh),김권우(Kwon-Woo Kim),한동열(Dong-Yeol Han),이승호(Seung-Ho Lee),이준탁(John-Tark Lee),이권순(Kwon-Soon Lee) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.7
A newly proposed FR (Face Recognition) approach with a weighted regularization parameter based on the conventional R-LDA (Regularized Linear D iscriminant A nalysis) method was presented in this paper. SSS (Sm all Sample Size) problem refers to the total number of training samples is less than the dimension of face feature space and all the scatter matrices of LDA are singular. Therefore, it is impossible to apply the LDA algorithm to the FR. In this paper, it was attempted to optimize the revised Fisher's criterion with a weighted regularization parameter as a solution of the SSS problem. Simulations using ORL (Olivetti Research Lab) database in MATLAB were simulated in order to evaluate the recognition performance of the proposed FR. In addition, the recognition performance of the proposed approach was compared to the ones of the well-known conventional methods such as Eigenfaces and R-LDA, which were established in this paper.