Application of Three Convolutional Neural Network Algorithms for Occluded Face Identification and Recognition for System Security
DOI:
https://doi.org/10.54536/ajmri.v1i5.740Keywords:
Computer Vision, CNN Pre-Trained, Deep Learning, Face Recognition, OcclusionAbstract
Deep Learning techniques in computer vision have become indispensable elements in biometric systems, especially face recognition. Facial recognition can be reliably used as an identification and authentication tool for premises or network access security. The masks wearing, which is one of the problems of concealment, are nowadays part of our habits for preventing COVID-19 disease, which leads to an obstruction of facial recognition. Occulted face recognition is one of the most challenging problems biometrics deals with. This paper presents convolution neural network algorithms for occluded face recognition. Our study presents a robust method using algorithms such as ResNet-50, VGG-19, and DenseNet-201 to contribute to occluded face recognition. Various parameters are used for this experiment, such as the cross-entropy used as a loss function and optimization algorithms adapted to deep learning. These include the SGD, Adam, and RMSProp optimizers. The convolution neural network algorithms were evaluated on the AR database. This experiment gave results that ranged from 94.81 to 99.81% for SGD, from 0 to 96.92 for Adam, and finally from 0 to 96.92 for RMSProp. DenseNet-201 algorithm using the SGD optimizer obtained the best score with 99.81%, and all the performance metrics used such as accuracy, MSE, F-score, recall, and MCC were used to confirm this good performance.
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Copyright (c) 2022 Mamadou Diarra, Ayikpa Kacoutchy Jean, Ballo Abou Bakary, Kouassi Brou Medard
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