Downsampling sparse representation and discriminant information aided occluded face recognition

In this paper, a strategy is proposed to deal with a challenging research topic, occluded face recognition. Our approach relies on sparse representation on downsampled input image to first locate unoccluded face parts, and then exploits the linear discriminant ability of those pixels to identify the input subject. The advantages and novelties of our method include, 1) since the sparse representation based occlusion detection is conducted on dowsampled image, our algorithm is much faster than classic SRC; 2) the discriminant information learned from training samples is combined with sparse representation to recognize occluded face for the first time. The verification experiments are conducted on both simulated block occlusion images and genuine occluded images.