Abstract
The rapid spreading of Coronavirus disease 2019 (COVID-19) is a major health risk that the whole world is facing for the last two years. One of the main causes of the fast spreading of this virus is the direct contact of people with each other. There are many precautionary measures to reduce the spread of this virus; however, the major one is wearing face masks in public places. Detection of face masks in public places is a real challenge that needs to be addressed to reduce the risk of spreading the virus. To address these challenges, an automated system for face mask detection using deep learning (DL) algorithms has been proposed to control the spreading of this infectious disease effectively. This work applies deep convolution neural network (DCNN) and MobileNetV2-based transfer learning models for effectual face mask detection. We evaluated the performance of these two models on two separate datasets, i.e., our developed dataset by considering real-world scenarios having 2500 images (dataset-1) and the dataset taken from PyImage Search Reader Prajna Bhandary and some random sources (dataset-2). The experimental results demonstrated that MobileNetV2 achieved 98% and 99% accuracies on dataset-1 and dataset-2, respectively, whereas DCNN achieved 97% accuracy on both datasets. Based on our findings, it can be concluded that the MobileNetV2-based transfer learning model would be an alternative to the DCNN model for highly accurate face mask detection.