Symmetrical PCA in face recognition

Facial symmetry is a useful natural characteristic of facial images, which can help in the development of face-oriented recognition technology and algorithms. The paper applies it to face recognition after introducing mirror images. By combining PCA with the even-odd decomposition principle, a new algorithm called symmetrical principal component analysis is proposed, in which different energy ratios of even/odd symmetrical principal components and their different sensitivities to pattern variations are employed for feature selection. This algorithm has two outstanding advantages. Firstly, it effectively improves the stability of features and remarkably raises the recognition rate. Secondly, it greatly saves computational cost as well as storage space.