B2DPCA vs B2DLDA: Face Feature Extraction Based on Image Matrix

In this paper, the bilateral two-dimensional principle component analysis (B2DPCA) and bilateral two-dimensional linear discriminant analysis (B2DLDA) are proposed to extract face feature by directly projecting the image matrix. Experimental results on the ORL and PIE face database are performed to test and evaluate the proposed algorithm. The results show that the LDA-based methods outperforms the PCA-based methods, and the two-dimension method outperforms the traditional one-dimensional methods. As opposed to one-dimensional methods, the two-dimensional methods directly extract the proper features from image matrices, while overleaping the process of turning image matrices into vectors, avoid the loss of some structural information residing in original 2D images.