Analysis of WD Face Dictionary for Sparse Coding Based Face Recognition

  • Shejin Thavalengal
  • Anil Kumar Sao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


This paper deals with the analysis of WD Face dictionary for sparse coding based face recognition. WD (weighted decomposition) Face dictionary emphasizes subject specific unique information of a person. This dictionary has an advantage to adapt to the nature of training images. In the resultant dictionary rows are uncorrelated, which is an essential criterion for dictionary to ensure sparse representation of coefficient vector. The range of sparsity determined by calculating the lower and upper bounds of sparse recovery of coefficient vector for WD Face dictionary exhibits its capability to sparsely represent a test image as a linear combination of training images, even when available training images are small in number. Experimental results solidify our proposal that sparse coding based face recognition with WD Face dictionary is preferable to the existing face recognition techniques.


Sparse coding dictionary face recognition 


  1. 1.
    Black Jr., J.A., Gargesha, M., Kahol, K., Kuchi, P., Panchanathan, S.: A framework for performance evaluation of face recognition algorithms. ITCOM, Internet Multimedia Systems II, 163–174 (July 2002)Google Scholar
  2. 2.
    Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. on Information Theory 52(2), 489–509 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Candes, E.J.: The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique 346(910), 589–592 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59(8), 1207–1223 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Deng, W., Hu, J., Guo, J.: Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(9), 1864–1870 (2012)CrossRefGoogle Scholar
  6. 6.
    Donoho, D.L.: Compressed sensing. IEEE Trans. on Information Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Elad, M.: Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing. Springer, New York (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gao, S., Tsang, I.W.-H., Chia, L.-T.: Sparse representation with kernels. IEEE Trans. on Image Processing 22(2), 423–434 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar
  10. 10.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley (2001)Google Scholar
  11. 11.
    Juditsky, A., Nemirovski, A.: On verifiable sufficient conditions for sparse signal recovery via l 1 minimization. Mathematical Programming 127, 57–88 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Sao, A.K., Yegnanarayana, B.: Analytic phase-based representation for face recognition. In: Seventh International Conference on Advances in Pattern Recognition, Kolkata, India, pp. 453–456 (February 2009)Google Scholar
  13. 13.
    Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  14. 14.
    Little, D., Krishna, S., Black, J., Panchanathan, S.: A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, USA, vol. 2, pp. 89–92 (March 2005)Google Scholar
  15. 15.
    Shejin, T., Sao, A.K.: Dictionary for sparse coding based pose invariant face recognition. unpublishedGoogle Scholar
  16. 16.
    Shejin, T., Sao, A.K.: Significance of dictionary for sparse coding based face recognition. In: 11th International Conference of the Biometrics Special Interest Group, pp. 1–6 (2012)Google Scholar
  17. 17.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Towards a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(2), 372–386 (2012)CrossRefGoogle Scholar
  18. 18.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)CrossRefGoogle Scholar
  19. 19.
    Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: 2011 IEEE International Conference on Computer Vision, Barcelona, Spain, pp. 471–478 (November 2011)Google Scholar
  20. 20.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computer Survey 35(4), 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shejin Thavalengal
    • 1
  • Anil Kumar Sao
    • 1
  1. 1.School of Computing and Electrical EngineeringIndian Institute of Technology MandiIndia

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