Face Mask Detection using MediaPipe Facemesh
B. Thaman, Thanh-Khiet Cao, Nicholas Caporusso
Abstract
Recently, face masks have received increasing attention due to the COVID-19 pandemic, as their correct use can reduce and prevent the spread of outbreaks. Thus, several research studies focused on developing new strategies for identifying if individuals are wearing a face mask before they can be admitted into public spaces, buildings, and transportation systems. In this paper, we present an alternative approach to face mask detection pipeline for automatically detecting whether an individual is equipped with a face mask. Our proposed solution utilizes MediaPipe, a popular image segmentation and object detection machine learning model designed especially for cross-platform operation, with specific regard to mobile devices. We present the architecture of our pipeline, detail its operation, and report the results of an evaluation study in which we analyzed the performance of our model in real-world scenarios.