Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment

@article{Zhuang2014SparseIL,
  title={Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment},
  author={Liansheng Zhuang and Tsung-Han Chan and Allen Yuqing Yang and S. Shankar Sastry and Yi Ma},
  journal={International Journal of Computer Vision},
  year={2014},
  volume={114},
  pages={272-287}
}
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 representation based classification (SRC) framework. The new algorithm 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 information traditionally provided by multiple gallery images, a sparse illumination learning and… 
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