Mask Compliance Detection on Facial Images

Garbagna, Lorenzo and Burrows, Holly and Babu Saheer, Lakshmi and Zarrin, Javad (2022) Mask Compliance Detection on Facial Images. In: SAI Computing Conference 2022, Online.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

The Covid19 pandemic has significantly changed our ways of living. Government authorities around the world have come up with safety regulations to help reduce the spread of this deadly virus. Covering the mouth and nose using facial masks is identified as an effective step to suppress the transmission of the infected droplets from one human to the other. While the usage of facial masks has been a common practice in several Asian societies, this practice is fairly new to the rest of the world including modern western societies. Hence, it can be noticed that the facial masks are either worn incorrectly (or sometimes not worn) by a significant number of people. Given the fact that the majority of the world population is only getting accustomed to this practice, it would be essential for surveillance systems to monitor if the general population is abiding by the regulatory standards of correctly wearing a facial mask. This paper uses deep learning algorithms to track and classify face masks. The research proposes a mask detection model based on Convolutional Neural Networks to discern between a correct usage of facial masks and its incorrect usages or even lack of it. Different architectures have been tested (even on real-time video streams) to obtain the best accuracy of 98.9% over four classes. These four classes include correctly worn, incorrectly worn on the chin, incorrectly worn on mouth and chin, and not wearing a mask at all. The novelty of this work is in the detection of the type of inaccuracy in wearing the face mask rather than just detecting the presence or absence of the same.

Item Type: Conference or Workshop Item (Paper)
Keywords: Deep Learning, Computer Vision, Face Masks Detection, COVID-19
Faculty: COVID-19 Research Collection
Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 13 Jun 2022 09:43
Last Modified: 28 Jul 2022 10:25
URI: https://arro.anglia.ac.uk/id/eprint/707682

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