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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 683494, 11 pages
http://dx.doi.org/10.1155/2014/683494
Research Article

A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

1School of Information Science and Technology, Northwest University, Xi’an 710069, China
2Department of Mathematics, Northwest University, Xi’an 710069, China

Received 3 October 2013; Revised 19 December 2013; Accepted 28 December 2013; Published 13 February 2014

Academic Editor: Chung-Hao Chen

Copyright © 2014 Zhao Jian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Wright, A. Yang, A. Ganesh et al., “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009. View at Google Scholar
  2. Y. Su, S. G. Shan, X. L. Chen et al., “Hierachical ensemble of global and local classifier for face recognition,” IEEE Transactions on Image Processing, vol. 9, no. 2, pp. 273–292, 2009. View at Google Scholar
  3. A. Yang, A. Ganesh, S. Sastry, and Y. Ma, “Fast l1-minimization algorithms and an application in robust face recognition: a review,” in Proceedings of the International Conference on Image Processing, 2010.
  4. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1031–1044, 2010. View at Google Scholar
  5. E. J. Candès, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207–1223, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  6. E. J. Candès, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207–1223, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  7. A. Martinez, “Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748–763, 2002. View at Google Scholar
  8. D. Donolo and M. Elad, “Optimal sparse representation in general(nonorthogonal) dictionaries via minimization,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 100, no. 5, pp. 2197–2202, 2003. View at Google Scholar
  9. D. L. Donoho, “For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution,” Communications on Pure and Applied Mathematics, vol. 59, no. 6, pp. 797–829, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  10. A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Review, vol. 51, no. 1, pp. 34–81, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  11. S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Review, vol. 43, no. 1, pp. 129–159, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  12. A. Yang, M. Gastpar, R. Bajcsy, and S. Sastry, “Distributed sensor perception via sparse representation,” Proceedings of the IEEE, vol. 98, no. 6, pp. 1077–1088, 2010. View at Google Scholar
  13. D. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proceedings of the National Academy of Sciences, vol. 106, no. 45, pp. 18914–18919, 2009. View at Google Scholar
  14. A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183–202, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. F. Sanja, D. Skocaj, and A. Leonardis, “Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 3, 2006. View at Google Scholar
  16. J. Wright and Y. Ma, “Dense error correction via 1-minimization,” IEEE Transactions on Information Theory, vol. 56, no. 7, pp. 3540–3560, 2010. View at Publisher · View at Google Scholar · View at MathSciNet