WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19
Zekun Wang, Pengwei Wang, Peter C. Louis, Lee E. Wheless, Yuankai Huo

TL;DR
WearMask is a fast, in-browser, serverless face mask detection system that works on common devices via web browsers, offering privacy, low cost, and high speed without software installation.
Contribution
It introduces a novel edge-computing framework combining YOLO, NCNN, and WebAssembly for accessible, fast face mask detection on any internet-connected device.
Findings
Achieves high detection speed in-browser
Requires minimal hardware resources
Operates without software installation
Abstract
The COVID-19 epidemic has been a significant healthcare challenge in the United States. According to the Centers for Disease Control and Prevention (CDC), COVID-19 infection 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 all respiratory infections. Therefore, many face mask detection and monitoring systems have been developed to provide effective supervision for hospitals, airports, publication transportation, sports venues, and retail locations. However, the current commercial face mask detection systems are typically bundled with specific 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…
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Taxonomy
TopicsFace recognition and analysis · COVID-19 diagnosis using AI · Infection Control and Ventilation
