@Article{iasc.2022.018042, AUTHOR = {Qazi Mudassar Ilyas, Muneer Ahmad}, TITLE = {An Enhanced Deep Learning Model for Automatic Face Mask Detection}, JOURNAL = {Intelligent Automation \& Soft Computing}, VOLUME = {31}, YEAR = {2022}, NUMBER = {1}, PAGES = {241--254}, URL = {http://www.techscience.com/iasc/v31n1/44289}, ISSN = {2326-005X}, 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.}, DOI = {10.32604/iasc.2022.018042} }