WearMask: Fast in-browser face mask detection with serverless edge computing for COVID-19

Abstract

The COVID-19 epidemic has been a significant healthcare challenge in the United States. COVID-19 is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effective, and convenient method of blocking 80% of respiratory infections. Therefore, many face mask detection systems have been developed to supervise hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial solutions are typically bundled with software or hardware, impeding public accessibility. In this paper, we propose an in-browser serverless edge-computing-based face mask detection solution, called Web-based efficient AI recognition of masks (WearMask), which can be deployed on common devices (e.g., cell phones, tablets, computers) with internet connections using web browsers. The serverless edge-computing design minimizes the hardware costs (e.g., specific devices or cloud computing servers). It provides a holistic edge-computing framework for integrating (1) deep learning models (YOLO), (2) high-performance neural network inference computing framework (NCNN), and (3) a stack-based virtual machine (WebAssembly). For end-users, our solution has advantages of (1) serverless edge-computing design with minimal device limitation and privacy risk, (2) installation-free deployment, (3) low computing requirements, and (4) high detection speed. Our application has been launched with public access at facemask-detection.com.