Mask Recognition with Computer Vision in the Age of a Pandemic

Abstract

In the current age of coronavirus, monitoring and enforcing correct mask-wearing regulation in public spaces is of para- mount importance. Specifically, there is a need to monitor whether people wear masks and whether they wear them cor- rectly. However, there is a lack of automated systems to rec- ognize 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 convo- lutional neural network to recognize images of people wear- ing 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 auto- mated system to screen people entering indoor spaces, and can replace current manual, time-consuming, temperature- screening practices. Such applications can serve as an im- portant tool to help reduce transmission rates during the cur- rent pandemic.