Fuzzy reasoning model to improve face illumination invariance
|Author:||Oulefki, Adel1; Mustapha, Aouache1; Boutellaa, Elhocine1,2;|
1Centre de Développement des Technologies Avancées, Baba Hassen, Algeria
2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019091328162
|Publish Date:|| 2019-09-13
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.
|Pages:||421 - 428|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
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).
© 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.