Illumination-robust face recognition based on deep convolutional neural networks architectures - Ridha Ilyas Bendjillali; Mohammed Beladgham; Khaled Merit; Abdelmalik Taleb-Ahmed (Indonesian Journal of Electrical Engineering and Computer Science, 2020)
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.