An enhanced deep learning model for automatic face mask detection

Document Type

Article

Publication Date

1-1-2022

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.

Keywords

Face mask detection, Image classification, Deep learning, MobileNetV2, Sustainable health, COVID-19 pandemic, Machine intelligence

Publication Title

Intelligent Automation and Soft Computing

Divisions

fsktm

Funders

Deanship of Scientific Research at King Faisal University, Saudi Arabia [Grant No: 206128]

Volume

31

Issue

1

Publisher

Tech Science Press

Publisher Location

871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052 USA

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