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Symmetrical PCA and Its Application to Face Recognition

Facial symmetry is a useful natural characteristic of facial images. This paper will apply it to face recognition after introducing mirror images. By combining the K L expansion with the even odd decomposition principle, a new algorithm called Symmetrical Principal Component Analysis is proposed. In the algorithm, images are firstly decomposed into even symmetrical images and odd symmetrical ones. After that, even/odd symmetrical principal components are respectively extracted through K L expansions, and then selected according to their energy ratios in faces and sensitivities to pattern variations. Both theoretical analysis and experimental results demonstrate that this algorithm has two outstanding advantages. Firstly, it remarkably raises the recognition rate. Secondly, it greatly saves the computational cost as well as the storage space.