Symmetry based two-dimensional principal component analysis and its application to face recognition
基于对称的二维主成分分析及其在人脸识别中的应用

This paper presented a Symmetry based Two-Dimensional Principal Component Analysis (STDPCA) on the basis of this idea of symmetry, which was introduced into Two-Dimensional Principal Component Analysis (TDPCA). Firstly, facial image was divided into the even symmetrical image and the odd symmetrical image. Then TDPCA was performed in the even symmetrical image and the odd symmetrical image for feature extraction, respectively. Therefore, STDPCA used effectively not only the advantages of TDPCA, but also the symmetrical properties of facial images. The experimental results on ORL and YALE database show the efficiency of STDPCA.