Robust Face Recognition Under Varying Illumination and Occlusion via Single Layer Networks

Feature extraction plays a significant role in face recognition, it is desired to extract robust feature to eliminate the effect of variations caused by illumination and occlusion. Motivated by convolutional architecture of deep learning and the advantages of KMeans algorithm in filters learning. In this paper, a simple yet effective face recognition approach is proposed, which consists of three components: convolutional filters learning, nonlinear transformation and feature pooling. Concretely, firstly, KMeans is employed to construct the convolutional filters quickly on preprocessed image patches. Secondly, hyperbolic tangent is applied for nonlinear transformation on the convoluted images. Thirdly, multi levels of spatial pyramid pooling is utilized to incorporate spatial geometry information of learned features. Recognition phase only requires an efficient linear regression classifier. Experimental results on two representative databases AR and ExtendedYaleB demonstrate strong robustness of our method against real disguise, illumination, block occlusion, as well as pixel corruption.