Abstract An algorithm is proposed, called symmetrical two dimensional principal component analysis (S2DPCA). It is based on the theory of function decomposition in algebra and mirror symmetry in geometry. Firstly, mirror transform is applied to images. Then, the images are decomposed into even and odd symmetrical images, and 2DPCA are performed on the even and odd images respectively. According to the idea of selective ensemble, the more discriminant eigenvectors of the even and odd image space are selected to construct the final eigenspace. Finally, the even and odd 2DPCA features are gotten by projecting samples onto the eigenspace. Both theoretical analysis and experimental results demonstrate that the algorithm can enlarge the number of training samples and raise the recognition rate.
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2008, Vol. 21 
