Applications of Machine Learning for Face Mask Detection During COVID-19 Pandemic

Covid-19 pandemic has forced us to adapt to the new lifestyle. World Health Organization (WHO) recommends that people should adhere to the public health expert’s guidelines to fight against the spread of Covid-19. The most essential Covid-19 guideline has been the use of facemask which has been enforced throughout the globe and has proven to contain the spread of corona virus. The proposed study aims at examining the detection of mask usage by people through Machine learning approach. The research employs binary classification problem to detect and classify people wearing masks from the people not-wearing masks. Three machine learning models namely InceptionV3, VGGNet and Resnet have been adapted in this research for pre-processing the input images. Similarly, XGBoost, Random Forest and fully connected DNN models have been used for decoding and classification. Performance evaluation has also been done for the different models and a comparison of the performances has been carried out as a part of the research. The results obtained through performance evaluation technique showed that ResNet+Fully Connected DNN is the best among the developed models where the precision was 99.73%, accuracy was 99.7%, F1 Score was 99.69% and the recall score was 99.66%.