The Face Recognition Method of the Two-Direction Variation of 2DPCA
This paper first discusses the relationship of Principal Component Analysis (PCA) and 2-Dimensional PCA (2DPCA). For 2DPCA eliminating some covariance information which can be useful for recognition and needing more coefficient for image presentation. The face recognition method of the Two-Direction Variation of 2DPCA (TDV2DPCA) is proposed, which makes use of more discriminant information in the variation of 2DPCA, and reduces the coefficients for image presentation in the way of two direction dimensionality reduction. The experiments on both of ORL and FERET face bases show improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the BD2DPCA and PCA in terms of the recognition accuracy.