IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Face Mask Classification Based on Deep Learning Framework

Face Mask Classification Based on Deep Learning Framework
View Sample PDF
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

Purchase

View Face Mask Classification Based on Deep Learning Framework on the publisher's website for pricing and purchasing information.

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.

Related Content

Xiao Wen Lu, Youssef Tliche, Mohammadali Vosooghidizaji, Atour Taghipour. © 2023. 13 pages.
Anukruti Mathur, Anushree Sah, Saurabh Rawat. © 2023. 19 pages.
Kamalendu Pal. © 2023. 27 pages.
Kamalendu Pal. © 2023. 34 pages.
Ilker Kara, Emre Hasgul. © 2023. 14 pages.
Fabienne T. Cadet. © 2023. 10 pages.
Yatri Davda. © 2023. 18 pages.
Body Bottom