Abstract
Abstract
Due to the rapid global spread of the COVID-19 outbreak, people's daily lives have been severely disrupted. To manage the outbreak, one solution is to mandate the wearing of face masks in public places. A face detection system that is automated and effective is therefore essential for enforcing this requirement. This paper presents a face mask detection model for images that classifies them as either "with mask" or "without mask." The three datasets Real-World Masked Face Dataset (RMFD), Simulated Masked Face Dataset (SMFD), and Labeled Faces in the Wild (LFW) are used to train and test the model, and attained a performance accuracy rate of 99.72% for first dataset, and 100% for the second and third datasets. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.
Publisher
Research Square Platform LLC
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