(2D)2PCALDA: An efficient approach for face recognition

摘要

In this paper, we propose a novel method for image feature extraction, namely, (2D)2PCALDA. This method directly extracts the optimal projective vectors from 2D image matrices by simultaneously considering row-direction 2DPCA and column-direction 2DLDA. The proposed method not only avoids huge feature matrix problem in 2DPCA and 2DLDA, but also take full advantage of the discriminant information and descriptive information of the images. Experiment results on Yale face database and ORL face databases demonstrate the effectiveness and robustness of the proposed method.