Abstract:

this paper the vertical symmetry of face, the characteristics of PCA and 2DPCA are discussed. And it is proved that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the PCA covariance matrix, and eliminates the covariance information that can be useful for recognition. A reshaped 2DPCA algorithm based on the vertical symmetry of face (S2DPCA) is proposed which can make the most useful of the covariance discriminate information, represents a face with  fewer coefficients. The experiments on the ORL face bases show it reduces the computational complexity compared with PCA, improve the recognition rate of face compared with PCA and 2DPCA, and is also superior to the traditional algorithms (ICA, eigenfaces and Keinel eigenfaces), and shows a face image with fewer coefficients.

Key words: PCA(principal component analysis);twodimensional PCA(2DPCA);face recognition;face representation