Mask Recognition in the Covered Safe Entry Scanner

SARS-CoV-2 is a highly contagious, airborne-transmission, virus that can be spread by people who do not have obvious symptoms. In 2020, that combination of features forced much of the world to impose a wide variety of forms of social distancing, ranging from simple recommendations restricting how shared spaces can be used to rigidly enforced quarantines. It is unclear how much distancing is enough, but it is clear that the economic and emotional costs of distancing are high. Fortunately, consistent use of simple face masks dramatically reduces the probability of others becoming infected. The catch is that a significant fraction of the US population either is refusing to wear masks or is wearing masks in ways that render them ineffective. For example, it is problematic for a shop owner to prevent potential customers who are not properly masked from entering their store. Thus, we have created the Covered Safe Entry Scanner - an open source system that uses image processing methods to automatically check for proper use of masks and potentially deny entry to those who do not comply. This paper describes the design, algorithms, and performance of the mask recognition system.