Multimodal CNN-Based System For Mask And Maskless Face Detection

Abstract

Face masks existed for much longer before the pandemic corresponding to COVID-19, whereverstaff in several sectors, such as medical, chemical, and nuclear, needed to wear masks throughout duties.Following the pandemic caused by the COVID-19 virus, most countries requested publicly covering thenose and mouth as vital life to keep the communities safe. However, 24/7 human superintendence is almostimpossible.In this paper, an efficient and automatic multimodal face mask detection was developed. The model wasengineered based on intensive investigations, where first, the performance of two well-known deep learningmodels, particularly MobileNetV2 and VGG19, was investigated. Next, the performance was furtherimproved using the late fusion principle. Four datasets consisting of roughly 6K, 12K, 4k, and 4k images,respectively, are used to confirm the results robustness of the developed model. Overall, the results of theexperimental works showed that fusion leads to a more stable and outperforming model compared to fivebase CNN models, i.e., MobileNetV2, VGG19, and three sequent models.