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Real-Time Face Mask Detection using Deep Learning

by Md. Towhidul Islam Robin, Md. Samiul Islam, Ahmed Abdal Shafi Rasel, Mehedi Hasan
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 40
Year of Publication: 2023
Authors: Md. Towhidul Islam Robin, Md. Samiul Islam, Ahmed Abdal Shafi Rasel, Mehedi Hasan
10.5120/ijais2023451944

Md. Towhidul Islam Robin, Md. Samiul Islam, Ahmed Abdal Shafi Rasel, Mehedi Hasan . Real-Time Face Mask Detection using Deep Learning. International Journal of Applied Information Systems. 12, 40 ( June 2023), 54-58. DOI=10.5120/ijais2023451944

@article{ 10.5120/ijais2023451944,
author = { Md. Towhidul Islam Robin, Md. Samiul Islam, Ahmed Abdal Shafi Rasel, Mehedi Hasan },
title = { Real-Time Face Mask Detection using Deep Learning },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2023 },
volume = { 12 },
number = { 40 },
month = { June },
year = { 2023 },
issn = { 2249-0868 },
pages = { 54-58 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number40/1138-2023451944/ },
doi = { 10.5120/ijais2023451944 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:37.979456+05:30
%A Md. Towhidul Islam Robin
%A Md. Samiul Islam
%A Ahmed Abdal Shafi Rasel
%A Mehedi Hasan
%T Real-Time Face Mask Detection using Deep Learning
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 40
%P 54-58
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

COVID-19 has made a severe impact on the world’s economy, health, education sector, and so on. Although the infection rate of the COVID-19 virus has dropped significantly in recent days, there is no scope to ignore precautionary measures like wearing masks in public. In this paper we proposed, a real-time Deep Learning based face detection system using a balanced dataset of over 6000 images divided into two classes named ‘With Mask’ and ‘Without Mask. The existing studies do not consider the data where people wear masks but not correctly, hence violating the safety measures. Our dataset is well-curated to detect people who do not wear masks properly. Our proposed CNN model not only overperformed other existing studies in terms of accuracy but also requires significantly less memory and time compared to other existing models. Our model achieved an accuracy rate of about 98.5% and it requires only 20 Mega Bytes of memory to deploy the model.

References
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