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Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination
doi:10.1109/cvpr.2013.455
dblp:conf/cvpr/ZhuangYZSM13
fatcat:mirhr2iyvbfojji3pt3zd6b7ii