Int J Comput Vis DOI 10.1007/s11263-014-0749-x Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment

Abstract Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse represen-tation based classification (SRC) framework. The new algo-rithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination infor-mation traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectivel