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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.
Comparison of Convolutional Neural Network Architectures for Face Mask Detection
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0121283
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 12, 2021.
Abstract: In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A cost-effective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely; AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.
Keywords: Convolution neural network; deep learning; transfer learning; computer vision; facemask detection; COVID-19
Siti Nadia Yahya, Aizat Faiz Ramli, Muhammad Noor Nordin, Hafiz Basarudin and Mohd Azlan Abu, “Comparison of Convolutional Neural Network Architectures for Face Mask Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 12(12), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121283
@article{Yahya2021,
title = {Comparison of Convolutional Neural Network Architectures for Face Mask Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121283},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121283},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {12},
author = {Siti Nadia Yahya and Aizat Faiz Ramli and Muhammad Noor Nordin and Hafiz Basarudin and Mohd Azlan Abu}
}
