Multi-view face recognition based on manifold learning and multilinear representation

We propose an improved Tensorfaces algorithm for multi-view face recognition which integrates multi-linear analysis, manifold learning and statistical clustering in one framework. The training face images from different views are first mapped into a 2-D space by the Locality Preserving Projections (LPP) method where statistical clustering is used to capture the view variability. Then a test image of an unknown view can be projected into this 2-D space, and the two closet views can be identified. We develop a modified tensor decomposition method by incorporating two closest views in the calculation of the identity coefficients. The proposed method is evaluated on a large database of multi-view face images that include the CMU PIE and Weizmann databases. Experimental results show that this method outperforms the original TensorFaces method.