Real-Time Face Mask Classification with Convolutional Neural Network for Proper and Improper Face Mask Wearing

Abstract

Since the discovery of COVID-19, the wearing of a face mask has been recognized as an effective means of curbing the spread of most infectious respiratory diseases. A face mask must completely enclose the lips and nose properly for effective prevention of the disease. Some people still refuse to wear the mask, either out of annoyance or difficulty, or they are just wearing it incorrectly, which diminishes the mask's effectiveness and renders it worthless. The deep learning models described in this research provide a mechanism for assessing whether a face mask is being worn correctly or incorrectly using images. For both training and testing, the suggested method makes use of MaskedFace-Net dataset that contains annotated photos of an individual's face with proper and improper masks. Threshold optimizations are applied to produce significant results of prediction when comparing ResNet50, MobileNetV2 and DenseNet121 models. It is observed that better performance can be achieved with having accuracy as the target evaluation metric and reaching accuracy levels of 97.6%, 99.0%, and 99.8% for ResNet50, DenseNet121, and MobileNetV2, respectively after threshold optimization. As an outcome, DenseNet121 outperformed the other evaluated models when accuracy, recall, and precision metrics were used to assess the testing set. The face mask categorization can be used to automatically monitor face masks in real-time in public locations like hospitals, airports, shopping complexes and congested spaces to verify compliance with the published guidelines by the higher authorities in a country, making the results valuable for future use.