An enhanced deep learning model for automatic face mask detection
The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPS) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social as...
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my.um.eprints.335542022-07-27T07:14:46Z http://eprints.um.edu.my/33554/ An enhanced deep learning model for automatic face mask detection Ilyas, Qazi Mudassar Ahmad, Muneer QA75 Electronic computers. Computer science The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPS) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an enhanced lightweight deep learning model. A surveillance camera is installed in a public place to detect the faces of people. We use MobileNetV2 as a lightweight feature extraction module since the current convolution neural network (CNN) architecture contains almost 62,378,344 parameters with 729 million floating operations (FLOPs) in the classification of a single object, and thus is computationally complex and unable to process a large number of face images in real time. The proposed model outperforms existing models on larger datasets of face images for automatic detection of face masks. This research implements a variety of classifiers for face mask detection: the random forest, logistic regression, K-nearest neighbor, neural network, support vector machine, and AdaBoost. Since MobileNetV2 is the lightest model, it is a realistic choice for real-time applications requiring inexpensive computation when processing large amounts of data. Tech Science Press 2022 Article PeerReviewed Ilyas, Qazi Mudassar and Ahmad, Muneer (2022) An enhanced deep learning model for automatic face mask detection. Intelligent Automation and Soft Computing, 31 (1). pp. 241-254. ISSN 1079-8587, DOI https://doi.org/10.32604/iasc.2022.018042 <https://doi.org/10.32604/iasc.2022.018042>. 10.32604/iasc.2022.018042 |
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QA75 Electronic computers. Computer science Ilyas, Qazi Mudassar Ahmad, Muneer An enhanced deep learning model for automatic face mask detection |
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The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPS) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an enhanced lightweight deep learning model. A surveillance camera is installed in a public place to detect the faces of people. We use MobileNetV2 as a lightweight feature extraction module since the current convolution neural network (CNN) architecture contains almost 62,378,344 parameters with 729 million floating operations (FLOPs) in the classification of a single object, and thus is computationally complex and unable to process a large number of face images in real time. The proposed model outperforms existing models on larger datasets of face images for automatic detection of face masks. This research implements a variety of classifiers for face mask detection: the random forest, logistic regression, K-nearest neighbor, neural network, support vector machine, and AdaBoost. Since MobileNetV2 is the lightest model, it is a realistic choice for real-time applications requiring inexpensive computation when processing large amounts of data. |
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Article |
author |
Ilyas, Qazi Mudassar Ahmad, Muneer |
author_facet |
Ilyas, Qazi Mudassar Ahmad, Muneer |
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Ilyas, Qazi Mudassar |
title |
An enhanced deep learning model for automatic face mask detection |
title_short |
An enhanced deep learning model for automatic face mask detection |
title_full |
An enhanced deep learning model for automatic face mask detection |
title_fullStr |
An enhanced deep learning model for automatic face mask detection |
title_full_unstemmed |
An enhanced deep learning model for automatic face mask detection |
title_sort |
enhanced deep learning model for automatic face mask detection |
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Tech Science Press |
publishDate |
2022 |
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http://eprints.um.edu.my/33554/ |
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1739828456306245632 |
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13.19449 |