Face Mask Detection using Deep Learning and Transfer Learning Models

The fast spread of Coronavirus (COVID-19) has created a global health crisis. The World Health organization (WHO) released several recommendations to help in preventing the spread of coronavirus. Wearing a mask in crowded venues is the most appropriate protective practice against COVID-19, according to the WHO. Keeping a close eye on people in public places is next to impossible, so identifying face masks becomes critical in the fight against COVID-19. Medical image analysis and classification are two areas where deep learning has lately become one of the widely used ways for enhancing performance. It can be utilized quite efficaciously to identify individuals who are not wearing a mask. The utilization of transfer learning models as a deep learning technique is also on the rise, and they work quite well. In our study, we used a total number of 7235 data images from an online dataset for face mask detection, which is done with two deep learning models and one transfer learning model. The deep learning-based CNN model using MaxPooling operation and AveragePooling operation achieved the accuracy of 95.78% and 95.36% respectively. Contradictory to that, the transfer learning-based MobileNetV2 model achieved an accuracy of 99.10%.