Abstract: Practical face recognition systems are sometimes confronted with low-resolution face images. To address this problem, a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image is presented. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analyses (PCA) based features of one sample per class high-resolution (HR) face images. The obtained features from PCA are not good enough for dimensionality reduction and computational complexity when large set of databases are taken into consideration. To overcome that problem Kernel PCA is introduced. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the KPCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the ORL database show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.
Keywords: Kernel-principal component analysis, Canonical correlation analysis, Face recognition, Radial basis function, Super resolution