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Face Mask Classification Based on Deep Learning Framework

Face Mask Classification Based on Deep Learning Framework
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Author(s): Safa Teboulbi (Monastir University, Tunisia), Seifeddine Messaoud (Monastir University, Tunisia), Mohamed Ali Hajjaji (Monastir University, Tunisia) and Abdellatif Mtibaa (Monastir University, Tunisia)
Copyright: 2022
Pages: 14
Source title: Advanced Practical Approaches to Web Mining Techniques and Application
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Zdzislaw Polkowski (Wroclaw University of Economics, Poland) and Bharat Bhushan (Sharda University, India)
DOI: 10.4018/978-1-7998-9426-1.ch009

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Abstract

Since the infectious coronavirus disease (COVID-19) was first reported in Wuhan, it has become a public health problem around the world. This pandemic is having devastating effects on societies and economies. Due to the lack of health resources in a short period, all countries and continents are likely to face particularly severe damage that could lead to a large epidemic. Wearing a face mask that stops the transmission of droplets in the air can still be helpful in combating this pandemic. Therefore, this chapter focuses on implementing a face mask detection model as an embedded vision system. The six pre-trained models, which are MobileNet, ResNet-50, MobileNet-V2, VGG-19, VGG-16, and DenseNet, are used in this context. People wearing or not wearing masks were detected. After implementing and deploying the models, the selected models achieved a confidence score. Therefore, this study concludes that wearing face masks helps reduce the virus spread and fight this pandemic.

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