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Real-time face mask position recognition system based on MobileNet model by Md Hafizur Rahman; Mir Kanon Ara Jannat; Md Shafiqul Islam; Giuliano Grossi; Sathya Bursic; Md Aktaruzzaman is a scholarly article available to read on EtoBox.

What is Real-time face mask position recognition system based on MobileNet model about?

COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen’s Kappa) of thi

Author

Md Hafizur Rahman; Mir Kanon Ara Jannat; Md Shafiqul Islam; Giuliano Grossi; Sathya Bursic; Md Aktaruzzaman

Publisher

Elsevier BV

Published

2023

Language

EN