Sparse Representation Based Classification for Face Recognition by k-LiMapS Algorithm

  • Alessandro Adamo
  • Giuliano Grossi
  • Raffaella Lanzarotti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

In this paper, we present a new approach for face recognition that is robust against both poorly defined and poorly aligned training and testing data even with few training samples. Working in the conventional feature space yielded by the Fisher’s Linear Discriminant analysis, it uses a recent algorithm for sparse representation, namely k-LiMapS, as general classification criterion. Such a technique performs a local ℓ0 pseudo-norm minimization by iterating suitable parametric nonlinear mappings. Thanks to its particular search strategy, it is very fast and able to discriminate among separated classes lying in the low-dimension Fisherspace. Experiments are carried out on the FRGC version 2.0 database showing good classification capability even when compared with the state-of-the-art ℓ1 norm-based sparse representation classifier (SRC).

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Rabia, J., Hamid, R.: A survey of face recognition techniques. Journal of Information Processing Systems 5 (2009)Google Scholar
  3. 3.
    Perez, C., Cament, L., Castillo, L.E.: Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count. Pattern Recognition 44, 951–963 (2011)CrossRefGoogle Scholar
  4. 4.
    Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)CrossRefGoogle Scholar
  5. 5.
    Turker, M., Pentland, A.: Face recognition using Eigenfaces. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  6. 6.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  7. 7.
    He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using laplacianfaces. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 328–340 (2005)CrossRefGoogle Scholar
  8. 8.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Analysis and Machine Intelligence 31, 210–227 (2008)CrossRefGoogle Scholar
  9. 9.
    Donoho, D.L.: For most large underdetermined systems of linear equations the minimal ℓ1-norm solution is also the sparsest solution. Comm. Pure Appl. Math. 59, 797–829 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Candes, E., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59, 1207–1223 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Delac, K., Grgic, M. (eds.): Face Recognition. I-Tech Education and Publishing (2007)Google Scholar
  12. 12.
    Yan, S., Wang, H., Liu, J., Tang, X., Huang, T.: Misalignment-robust face recognition. IEEE Transactions on Image Processing 19, 1087–1096 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wagner, A., Wright, J.: Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans. Pattern Analysis and Machine Intelligence 34, 372–386 (2012)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  15. 15.
    Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye and mouth localization. International Journal of Pattern Recognition and Artificial Intelligence 23 (2009)Google Scholar
  16. 16.
    Adamo, A., Grossi, G.: A fixed-point iterative schema for error minimization in k-sparse decomposition. In: Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2011), pp. 167–172 (2011)Google Scholar
  17. 17.
    Phillips, P., Flynn, P., Scruggs, T., Bowyer, K.: Overview of the face recognition grand challenge. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Adamo
    • 1
  • Giuliano Grossi
    • 2
  • Raffaella Lanzarotti
    • 2
  1. 1.Dipartimento di MatematicaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di Scienze dell’InformazioneUniversità degli Studi di MilanoMilanoItaly

Personalised recommendations