ABSTRACT
In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the outbreak is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for static and real time videos has been presented which classifies the images as "with mask" and "without mask". The model is trained and evaluated using the Kaggle data-set. The gathered data-set comprises approximately about 4,000 pictures and attained a performance accuracy rate of 98%. The proposed model is computationally efficient and precise as compared to DenseNet-121, MobileNet-V2, VGG-19, and Inception-V3. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.
