Combination of two novel LDA-based methods for face recognition

Linear discriminant analysis (LDA)-based methods have been very successful in face recognition, yet little investigation has been done on the fusion of different LDA methods. Combination of two LDA methods which performed LDA on distinctly different subspaces may be effective in further improving the recognition performance. In this paper we first present two novel LDA-based methods, post-processed Fisherfaces (pFisherfaces) and bi-directional PCA plus LDA (BDPCA+LDABDPCA+LDA). pFisherfaces uses 2D-Gaussian filter to smooth classical Fisherfaces, and BDPCA+LDABDPCA+LDA is a LDA performed in the BDPCA subspace. Then we propose a combination framework of these two LDA-based approaches. Two popular face databases, the ORL and the FERET, are used to evaluate the efficiency of the proposed combination framework. The results of our experiments indicate that the combination framework is superior to pFisherfaces, BDPCA+LDABDPCA+LDA, and other appearance-based methods in terms of recognition accuracy.