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Shearlet Network-based Sparse Coding Augmented by Facial Texture Features for Face Recognition

  • Mohamed Anouar Borgi
  • Demetrio Labate
  • Maher El’Arbi
  • Chokri Ben Amar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

One open challenge in face recognition (FR) is the single training sample per subject. This paper addresses this problem through a novel approach that combine Shearlet Networks (SN) and PCA called (SNPCA). Shearlet Network takes advantage of the sparse representation (SR) properties of shearlets in biometric applications, Especially, for face coding and recognition. The main contributions of this paper are (1) the combination of the multi-scale representation which capture geometric information to derive a very efficient representation of facial templates, and the use of a PCA-based approach and (2) the design of a fusion step by a refined model of belief function based on the Dempster-Shafer rule in the context of confusion matrices. This last step is helpful to improve the processing of facial texture features. We compared our algorithm (SNPCA) against SN, a wavelet network (WN) implementation and other standard algorithms. Our tests, run on several face databases including FRGC, Extended Yale B database and others, shows that this approach yields a very competitive performance compared to wavelet networks (WN), standard shearlet and PCA-based methods.

Keywords

Shearlet Shearlets Network Sparse Coding Face Recognition 

References

  1. 1.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Automatic Interpretation and Coding of Face Images Using Flexible Models. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 743–756 (1997)CrossRefGoogle Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Leonardis, A., Bischof, H.: Robust recognition using eigenimages. Computer Vision and Image Understanding 78(1), 99–118 (2000)CrossRefGoogle Scholar
  4. 4.
    Chen, S., Shan, T., Lovell, B.C.: Robust face recognition in rotated eigenspaces. In: Proc. Intl Conf. Image and Vision Computing, New Zealand (2007)Google Scholar
  5. 5.
    Martinez, A.M.: Recognizing Imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)CrossRefGoogle Scholar
  6. 6.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.: Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks 14(1), 195–200 (2003)CrossRefGoogle Scholar
  7. 7.
    Yang, M., Zhang, D.: Jian Yang, Zhang, D.: Robust sparse coding for face recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2011)Google Scholar
  8. 8.
    Yang, M., Zhang, L., Yang, J., Zhang, D.: Regularized Robust Coding for Face Recognition. IEEE Trans. on Image Processing (2013)Google Scholar
  9. 9.
    Schwartz, W.R., da Silva, R.D., Davis, L.S., Pedrini, H.: A Novel Feature De-scriptor Based on the Shearlet Transform. In: IEEE International Conference on Image Processing, pp. 1033–1036 (2011)Google Scholar
  10. 10.
    Schwartz, W.R., Guo, H., Choi, J., Davis, L.S.: Face Identification Using Large Feature Sets. IEEE Transactions on Image Processing 21(4), 2245–2255 (2012)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Negi, P.S., Labate, D.: 3D discrete shearlet transform and video processing. IEEE Trans. Image Process. 21(6), 2944–2954 (2012)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Easley, G.R., Labate, D.: Critically sampled wavelets with composite dilations. IEEE Trans. Image Process. 21(2), 550–561 (2012)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Easley, G.R., Labate, D., Patel, V.: Directional multiscale processing of images using wavelets with composite dilations. J. Math. Imag. Vision (to appear, 2013)Google Scholar
  14. 14.
    Yi, S., Labate, D., Easley, G.R., Krim, H.: A Shearlet approach to Edge Analysis and Detection. IEEE Trans. Image Process. 18(5), 929–941 (2009)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Kutyniok, G., Labate, D.: Resolution of the wavefront set using continuous shearlets. Trans. Amer. Math. Soc. 361, 2719–2754 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Ben Amar, C., Zaied, M., Alimi, M.A.: Beta Wavelets. Synthesis and application to lossy image compression. Journal of Advances in Engineering Software 36(7), 459–474 (2005)CrossRefzbMATHGoogle Scholar
  17. 17.
    Zaied, M., Jemai, O., Ben Amar, C.: Training of the Beta wavelet networks by the frames theory: Application to face recognition. In: International Workshops on Image Processing Theory, Tools and Applications, Tunisia (November 2008)Google Scholar
  18. 18.
    Mercier, D., Cron, G., Denoeux, T., Masson, M.: Fusion of multi-level decision systems using the Transferable Belief Model. In: Proc. FUSION, Philadelphia, PA, USA (July 2005)Google Scholar
  19. 19.
    Denoeux, T.: Conjunctive and Disjunctive Combination of Belief Functions Induced by Non Distinct Bodies of Evidence. Artificial Intelligence 172, 234–264 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The Extended Cohn-Kande Dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression. In: Proc. CVPR, Workshop H CBA (2010)Google Scholar
  21. 21.
  22. 22.
    Thomaz, C.E., Giraldi, G.A.: A new ranking method for Principal Components Analysis and its application to face image analysis. Image and Vision Computing 28(6), 902–913 (2010)CrossRefGoogle Scholar
  23. 23.
    Georghiades, A.S., Belhumeur, P.N.: From Few to Many: Illumination Cone Mod-els for Face Recognition under Variable Lighting and Pose. IEEE Trans. on. PAMI 23(6), 643–660 (2001)CrossRefGoogle Scholar
  24. 24.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proc. CVPR 2005, vol. 1, pp. 947–954 (June 2005)Google Scholar
  25. 25.
    Yang, M., Zhang, L., Zhang, D.: Efficient Misalignment Robust Representation Representation for Real-Time Face Recognition Classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 850–863. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    Zhu, P., Zhang, L., Hu, Q., Shiu, S.C.K.: Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 822–835. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  27. 27.
    Su, Y., Shan, S.G., Chen, X.L., Gao, W.: Adaptive Generic Learning for Face Recogni-tion from a Single Sample per Person. In: Proc. CVPR (2010)Google Scholar
  28. 28.
    Mian, A.S., Bennamoun, M., Owens, R.: 2D and 3D Multimodal Hybrid Face Recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 344–355. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mohamed Anouar Borgi
    • 1
  • Demetrio Labate
    • 2
  • Maher El’Arbi
    • 1
  • Chokri Ben Amar
    • 1
  1. 1.REGIM: REsearch Groups on Intelligent MachinesUniversity of Sfax, ENISSfaxTunisia
  2. 2.Department of MathematicsUniversity of HoustonHoustonUSA

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