Masked Faces in Context (MASON) for Masked Face Detection and Classification

Abstract

As the SARS-CoV-2 virus mutated and spread around the world, scientists andpublic health officials were faced with the responsibility of making health recommen- dations as they studied the novel disease in real time. One such recommendation was the use of face masks of varying types as a method of reducing disease spread in public spaces. Evaluating the effectiveness of such measures requires accurate data collection of the proper facemask usage. The use of computer vision models to detect and clas- sify face mask usage can aid in the collection process by monitoring usage in public spaces. However, training these models requires accurate and representative datasets. Pre-COVID-19 datasets and synthetic datasets have limitations that affect the accu- racy of models in real world settings such as inaccurate representations of occlusion and limited variety of subjects, settings, and masks. In this work we present a new dataset Masked Faces in Context (MASON) of annotated real-world images focusing on the time period of 2020 to the present and baseline detection and classification models that outperforms the current state of the art. This dataset better snapshots mask wearing under covid with greater representation of different age groups, mask types, common occlusion items such as face shields, and face position. Our experiments demonstrate increased accuracy in face mask detection and classification.