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]
Tahleb Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 [IEMN-DOAE]

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: ...
Show more >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

Popular science :

Non

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL