Future of Information and Communication Conference (FICC) 2024
4-5 April 2024
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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 3, 2024.
Abstract: In the face of rampant global disease transmission, effective preventive strategies are imperative. This study tackles the challenge of ensuring compliance in crowded settings by developing a sophisticated face mask detection system. Utilizing MATLAB and the Cascade Object detector, the system focuses on detecting white surgical masks in frontal images. Training the system is critical for accuracy; therefore, cross-validation is employed due to limited data. The results reveal accuracies of 76.67% for initial training, 67.50% for a 9:11 cropping ratio, and 89.17% for a 9:4:7 cropping ratio, highlighting the system's remarkable precision in mask detection. Looking ahead, the system's adaptability can be further expanded to include various mask colors and types, extending its effectiveness beyond COVID-19 to combat a range of respiratory illnesses. This research represents a significant advancement in reinforcing preventive measures against future disease outbreaks, especially in densely populated environments, contributing significantly to global public health and safety initiatives.
Jamil Abedalrahim Jamil Alsayaydeh, Mohd Faizal bin Yusof, Chan Yoke Lin, Mohammed Nasser Mohammed Al-Andoli, Safarudin Gazali Herawan and Ida Syafiza Md Isa, “Precision Face Mask Detection in Crowded Environment using Machine Vision” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150325
@article{Alsayaydeh2024,
title = {Precision Face Mask Detection in Crowded Environment using Machine Vision},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150325},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150325},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Jamil Abedalrahim Jamil Alsayaydeh and Mohd Faizal bin Yusof and Chan Yoke Lin and Mohammed Nasser Mohammed Al-Andoli and Safarudin Gazali Herawan and Ida Syafiza Md Isa}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.