Fuzzy reasoning model to improve face illumination invariance
Oulefki, Adel; Mustapha, Aouache; Boutellaa, Elhocine; Bengherabi, Messaoud; Amine Tifarine, Ahmed (2017-09-04)
Oulefki, A., Mustapha, A., Boutellaa, E. et al. SIViP (2018) 12: 421. https://doi.org/10.1007/s11760-017-1174-8
© Springer-Verlag London Ltd. 2017. This is a post-peer-review, pre-copyedit version of an article published in SIViP. The final authenticated version is available online at: https://doi.org/10.1007/s11760-017-1174-8.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2019091328162
Tiivistelmä
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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