Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition

Abstract

In automatically recognizing human faces, it is an important problem how to extract the effective features from the corrupted face. This paper propose a new face recognition algorithm based on fusion of global and local Gaussian-Hermite moments (GHMs). Firstly, in order to solve the interference of noise on features, we use the GHMs of face image as facial feature. Second, we construct the face image spatial pyramid to extract the global and local features of the face, and then we compute scatter-ratio to seclect highly discriminative feature. Lastly we use sparse representation classifier to improve the robust of algorithm. Experiments on ORL, FERET and Yale A face databases reveal that the accuracy of proposed algorithm is better than traditional algorithm, especially when the face images are corrupted by salt&pepper noise.