Super-resolution for Face Recognition Based on Correlated Features and Nonlinear Mappings

For the problem of low recognition rate on low resolution face images, a super-resolution method for face recognition based on correlated features and nonlinear mappings is proposed in this paper. Canonical correlation analysis (CCA) is applied to establish the correlated subspaces between the features of high and low resolution face images, and radial base functions (RBFs) are employed to construct the nonlinear mappings between the features in the correlated subspaces. Finally, the super-resolved correlated feature in the high resolution space of a testing low resolution image is obtained for recognition. Compared with other methods, our method is robust to pose and expression variations and achieves higher recognition rate.