Illumination-robust face recognition based on deep convolutional neural networks architectures

Ridha Ilyas Bendjillali, Mohammed Beladgham, Khaled Merit, Abdelmalik Taleb-Ahmed


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.


Face Recognition (FR);modified contrast limited adaptive histogram equalization algorithm (M-CLAHE);VGG16;ResNet50;Inception-v3


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