University of Oulu

Oulefki, A., Mustapha, A., Boutellaa, E. et al. SIViP (2018) 12: 421. https://doi.org/10.1007/s11760-017-1174-8

Fuzzy reasoning model to improve face illumination invariance

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Author: Oulefki, Adel1; Mustapha, Aouache1; Boutellaa, Elhocine1,2;
Organizations: 1Centre de Développement des Technologies Avancées, Baba Hassen, Algeria
2Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019091328162
Language: English
Published: , 2018
Publish Date: 2019-09-13
Description:

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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Volume: 12
Issue: 3
Pages: 421 - 428
DOI: 10.1007/s11760-017-1174-8
OADOI: https://oadoi.org/10.1007/s11760-017-1174-8
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: 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).
Copyright information: © 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.