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)


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.


Shearlet Shearlets Network Sparse Coding Face Recognition 


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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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