Multi-modal face recognition based on intensity image and depth image

This paper proposes a method which combines global features of Two-Dimensional Principal Component Analysis(2DPCA)and Two-Dimensional Fisher Linear Discriminate Analysis(2DFLD)with local feature of Local Binary Pattern(LBP),and applies them in multi-modal face recognition based on 2D intensity image and 3D depth image.Then the normalized similarity scores of different methods will be fused by weighted sum rule.Experimental results show that LBP will improve the performance of 2DPCA and 2DFLD,and score fusion of 2D intensity images and 3D depth images makes improvement too,the highest recognition rate on CASIA3D database has achieved 94.68%.