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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 (2014)
BibTeX
@MISC{Zhuang14intj,
author = {Liansheng Zhuang and Tsung-han Chan and Allen Y. Yang and S. Shankar and Sastry Yi Ma and L. Zhuang and T. -h. Chan and S. S. Sastry and Y. Ma},
title = {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},
year = {2014}
}
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Abstract
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 effectively
