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Illumination-robust face recognition based ...
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Document type :
Article dans une revue scientifique
DOI :
10.11591/ijeecs.v18.i2.pp1015-1027
Title :
Illumination-robust face recognition based on deep convolutional neural networks architectures
Author(s) :
Bendjillali, Ridha Ilyas [Auteur correspondant]
Beladgham, Mohammed [Auteur]
Merit, Khaled [Auteur]
Taleb-Ahmed, Abdelmalik [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Journal title :
Indonesian Journal of Electrical Engineering and Computer Science
Pages :
1015-1027
Publisher :
IAES
Publication date :
2020
ISSN :
2502-4752
English keyword(s) :
Face recognition
Inception-v3
M-CLAHE
ResNet50
VGG16
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: ...
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In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520
Source :
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