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

Abstract : 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.
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Submitted on : Thursday, February 10, 2022 - 1:00:07 PM
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Ridha Ilyas Bendjillali, Mohammed Beladgham, Khaled Merit, Abdelmalik Taleb-Ahmed. Illumination-robust face recognition based on deep convolutional neural networks architectures. Indonesian Journal of Electrical Engineering and Computer Science, IAES, 2020, 18 (2), pp.1015-1027. ⟨10.11591/ijeecs.v18.i2.pp1015-1027⟩. ⟨hal-03564174⟩

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