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

Hamdi Jamel Bouchech, Sebti Foufou, Andreas Koschan, Mongi Abidi

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    Pages (from-to)8631-8654
    Number of pages24
    JournalMultimedia Tools and Applications
    Volume74
    Issue number19
    DOIs
    StatePublished - Oct 31 2015

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    Face recognition
    Lighting
    Fusion reactions

    Keywords

    • Bands selection
    • HGPP
    • IRIS-M
    • LGBPHS
    • MBLBP
    • POEM-WPCA
    • Subspectral images

    ASJC Scopus subject areas

    • Software
    • Media Technology
    • Hardware and Architecture
    • Computer Networks and Communications

    Cite this

    A kernelized sparsity-based approach for best spectral bands selection for face recognition. / Bouchech, Hamdi Jamel; Foufou, Sebti; Koschan, Andreas; Abidi, Mongi.

    In: Multimedia Tools and Applications, Vol. 74, No. 19, 31.10.2015, p. 8631-8654.

    Research output: Contribution to journalArticle

    Bouchech, Hamdi Jamel ; Foufou, Sebti ; Koschan, Andreas ; Abidi, Mongi. / A kernelized sparsity-based approach for best spectral bands selection for face recognition. In: Multimedia Tools and Applications. 2015 ; Vol. 74, No. 19. pp. 8631-8654.
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