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Fuzzy reasoning model to improve face illumination invariance

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Abstract

Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the automatic face recognition systems. This work tackles illumination variation challenge by proposing a new face image enhancement approach based on Fuzzy theory. The proposed Fuzzy reasoning model generates an adaptive enhancement which corrects and improves non-uniform illumination and low contrasts. The FRM approach has been assessed using four blind-reference image quality metrics supported by visual assessment. A comparison to six state-of-the-art methods has also been provided. Experiments are performed on four public data sets, namely Extended Yale-B, Mobio, FERET and Carnegie Mellon University Pose, Illumination, and Expression, showing very interesting results achieved by our approach.

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Acknowledgements

This research received funding from the Algerian Ministry of Higher Education and Scientific Research (AMHESR) via the National Research Fund project (AVVISA-FNR-2013-2016/003). The authors are with the Centre de Dévelopment des Technologies Avancées (CDTA).

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Authors and Affiliations

  1. Centre de Développement des Technologies Avancées, Baba Hassen, Algeria

    Adel Oulefki, Aouache Mustapha, Elhocine Boutellaa, Messaoud Bengherabi & Ahmed Amine Tifarine

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland

    Elhocine Boutellaa

Authors
  1. Adel Oulefki
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  2. Aouache Mustapha
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  3. Elhocine Boutellaa
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  4. Messaoud Bengherabi
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  5. Ahmed Amine Tifarine
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Correspondence to Adel Oulefki.

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Oulefki, A., Mustapha, A., Boutellaa, E. et al. Fuzzy reasoning model to improve face illumination invariance. SIViP 12, 421–428 (2018). https://doi.org/10.1007/s11760-017-1174-8

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