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International Journal of Computer Vision

, Volume 114, Issue 2–3, pp 272–287 | Cite as

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

  • Liansheng Zhuang
  • Tsung-Han Chan
  • Allen Y. YangEmail author
  • S. Shankar Sastry
  • Yi Ma
Article

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 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 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 recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

Keywords

Single-sample face recognition Illumination dictionary learning Sparse illumination transfer Face alignment Robust face recognition 

Notes

Acknowledgments

The work was supported in part by ARO 63092-MA-II, DARPA FA8650-11-1-7153, ONR N00014-09-1-0230, NSF CCF09-64215, NSFC No. 61103134 and 61371192, and the Science Foundation for Outstanding Young Talent of Anhui Province (BJ2101020001).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Liansheng Zhuang
    • 1
  • Tsung-Han Chan
    • 2
  • Allen Y. Yang
    • 3
    Email author
  • S. Shankar Sastry
    • 3
  • Yi Ma
    • 4
  1. 1.CAS Key Laboratory of Electromagnetic Space InformationUniversity of Science and Technology of ChinaHeifeiChina
  2. 2.Advanced Digital Sciences CenterSingaporeSingapore
  3. 3.Department of EECSUniversity of CaliforniaBerkeleyUSA
  4. 4.ShanghaiTech UniversityShanghaiChina

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