Identification of Medical Mask Use by Applying the Convolutional Neural Network Algorithm and the Gabor Filter with Multiclass Classification

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) causes global pandemics and makes countries around the world lock down fortourists. This action is required to prevent the spread of viruses that take 14 days to disappear. SARS-COV-2 can easily infect individuals through a droplet. Thus, the governments of every country worldwide recommend wearing medical masks to prevent the spread of viruses, as well as maintaining distance during activities with others and washing hands frequently. Medical masks become efficient if their application is precise, owing to a lack of knowledge and self-awareness to preserve their distance and wash their hands. This paper proposes a Convolutional Neural Network (CNN) with Gabor filter implementation. The simulation uses a mask on a dataset with over 70,000 individual photos. The results demonstrated that the proposed CNN-Gabor model in this work could effectively classify the position of the mask when compared to the CNN model without the Gabor filter.