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Regularized directional feature learning for face recognition

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Abstract

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach is augmented by regularization theory (RSN) in order to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-art methods.

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Acknowledgment

The authors would like to acknowledge the financial support of this work by grants from General Direction of scientific Research (DGRST), Tunisia, under the ARUB program. D. Labate acknowledges partial support by NSF DMS 1005799 and DMS 1008900.

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Authors and Affiliations

  1. Research Group on Intelligent Machines, University of Sfax, ENIS, BP 1173, Sfax, 3038, Tunisia

    Mohamed Anouar Borgi, Maher El’Arbi & Chokri Ben Amar

  2. Department of Mathematics, University of Houston, Houston, TX, 77204, USA

    Demetrio Labate

Authors
  1. Mohamed Anouar Borgi
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  2. Maher El’Arbi
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  3. Demetrio Labate
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  4. Chokri Ben Amar
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Correspondence to Mohamed Anouar Borgi.

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Borgi, M.A., El’Arbi, M., Labate, D. et al. Regularized directional feature learning for face recognition. Multimed Tools Appl 74, 11281–11295 (2015). https://doi.org/10.1007/s11042-014-2228-3

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  • DOI: https://doi.org/10.1007/s11042-014-2228-3

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