Advertisement

Learning a Sparse Representation for Robust Face Recognition

  • Weihua Ou
  • Xinge You
  • Pengyue Zhang
  • Xiubao Jiang
  • Ziqi Zhu
  • Duanquan Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Based on the assumption that occlusions have sparse representation on the nature pixel coordinate, Sparse Representation based Classification (SRC) [9] adopts an identity matrix as occlusion dictionary to deal with the occlusions or noises. However, this assumption is often violated in real applications, such as the faces are occluded by scarf. In this paper, we present an approach to learn an occlusion dictionary from the data. Thus, the occlusions have sparse representation on the learned occlusion dictionary and can be effectively separated from the occluded face images. Experimental results show our approach achieves better performance than SRC, while the computational cost is much lower.

Keywords

Face recognition occlusions dictionary learning learning sparse representation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on PAMI 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Deng, W., Hu, J., Guo, J.: Extended src: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on PAMI 34(9), 1864–1870 (2012)CrossRefGoogle Scholar
  4. 4.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on PAMI 23(6), 643–660 (2001)CrossRefGoogle Scholar
  5. 5.
    Kim, S., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4), 606–617 (2007)CrossRefGoogle Scholar
  6. 6.
    Lee, D., Seung, H., et al.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  7. 7.
    Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)CrossRefGoogle Scholar
  8. 8.
    Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on PAMI 31(2), 210–227 (2009)CrossRefGoogle Scholar
  10. 10.
    Yang, M., Zhang, L., Yang, J., Zhang, D.: Robust sparse coding for face recognition. In: Proc. of CVPR, pp. 625–632. IEEE (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weihua Ou
    • 1
    • 2
  • Xinge You
    • 1
  • Pengyue Zhang
    • 1
  • Xiubao Jiang
    • 1
  • Ziqi Zhu
    • 1
  • Duanquan Xu
    • 1
  1. 1.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Department of mathematicsHuaihua UniversityHuaihuaChina

Personalised recommendations