Masked Face Detection and Calibration with Deep Learning Models

Abstract

Under the COVID-19 pandemic, the demand that face detection devices should be enhanced to detect masked faces is imperative. In this study, we utilize several state-of-the-art face detection models and compare them on various unmasked and masked human face datasets. Moreover, by analyzing the results we obtain, we evaluate these disparate models and discover some problems. Attempting to overcome the problems discovered, we propose and implement several improvements, and acquire more results for analysis. At length, we propose some ideas for future research directions.