Comparative Analysis of Deep Learning Architectures for Masked Face Recognition: A Study of Performance and Robustness

Face recognition technology has been significantly impacted by the COVID-19 epidemic, among other parts of daily life. Facial recognition technologies, which were formerly employed for security, access control, and identity verification purposes, have actually become less reliable as a result of mask use. Although deep learning-based methods have demonstrated considerable promise in this field, it is still unknown how well various architectural approaches perform. The performance of five well-known deep learning architectures—ResNet50, VGG16, InceptionV3, MobileNetV2, and Xception—is compared in this study. We evaluate the architectures using a sizable dataset (LFW) made up of masked faces of various racial and gender compositions. According to our findings, the ResNet50 has a higher accuracy score of 98.3% compared to the Vgg16 architecture’s 98.2%, Xception’s 98, MobileNet’s 97.6 and Inception’s 97.4%. In order to determine the impact of various model elements, such as the quantity of convolutional layers, the quantity of parameters, and the time inference, we also perform an analysis study. Our findings show that a key factor in getting high accuracy for masked face recognition is the network’s depth.