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.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Deanship of Scientific Research at King Faisal University, Saudi Arabia [Grant No: 206128] |
| Uncontrolled Keywords: | Face mask detection; Image classification; Deep learning; MobileNetV2; Sustainable health; COVID-19 pandemic; Machine intelligence |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 27 Jul 2022 07:14 |
| Last Modified: | 27 Jul 2022 07:14 |
| URI: | http://eprints.um.edu.my/id/eprint/33554 |
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