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A kernelized sparsity-based approach for best spectral bands selection for face recognition

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Abstract

We study face recognition in unconstrained illumination conditions. A twofold contribution is proposed: First, the robustness of four state-of-the-art algorithms, namely Multi-block Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP), Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and Patterns of Oriented Edge Magnitudes (POEM-WPCA) against high illumination variation is studied. Second, we propose to enhance the performance of the four mentioned algorithms, which has been drastically decreased upon the day lighted face images provided by IRIS-M3 face database. For this purpose, we use visible narrow band subspectral images selected from the mentioned database. We formulate best spectral bands selection as a pursuit optimization problem wherein the vector of weights determining the importance of each visible spectral band is supposed to be sparse, and hence can be determined by minimizing its L1-norm. Several fusing approaches are then applied on selected best spectral bands using multi-scale and multi-orientation Gabor wavelets. The results highlight further the still challenging problem of face recognition in conditions with high illumination variation, as well as the effectiveness of our subspectral images based approach with its two components; bands selection and bands fusion, to increase the accuracy of the studied algorithms by at least 14 % upon the proposed database.

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Acknowledgments

This publication was made possible by NPRP grant # 4-1165-2-453 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. LE2i Laboratory, University of Burgundy, Dijon, France

    Hamdi Jamel Bouchech & Sebti Foufou

  2. Computer Science and Engineering, CENG, Qatar University, P.O. Box 2713, Doha, Qatar

    Hamdi Jamel Bouchech & Sebti Foufou

  3. Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, USA

    Andreas Koschan & Mongi Abidi

Authors
  1. Hamdi Jamel Bouchech
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  2. Sebti Foufou
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  3. Andreas Koschan
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  4. Mongi Abidi
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Correspondence to Hamdi Jamel Bouchech.

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Bouchech, H.J., Foufou, S., Koschan, A. et al. A kernelized sparsity-based approach for best spectral bands selection for face recognition. Multimed Tools Appl 74, 8631–8654 (2015). https://doi.org/10.1007/s11042-014-2350-2

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  • DOI: https://doi.org/10.1007/s11042-014-2350-2

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