ABSTRACT
In the current age of coronavirus, monitoring and enforcing correct mask-wearing regulation in public spaces is of paramount importance. Specifically, there is a need to monitor whether people wear masks and whether they wear them correctly. However, there is a lack of automated systems to recognize correct mask-wearing compliance. In this paper, we propose a computer-vision-based solution to the problem of mask-wearing monitoring. In particular, we propose a convolutional neural network to recognize images of people wearing masks correctly, people wearing masks incorrectly, and people not wearing masks at all. Our proposed model is shown to predict correct mask-wearing practices with over 98% accuracy. The model can be easily deployed as an automated system to screen people entering indoor spaces, and can replace current manual, time-consuming, temperaturescreening practices. Such applications can serve as an important tool to help reduce transmission rates during the current pandemic. © 2020, by the authors. All rights reserved.