An Explainable Transfer Learning Based Approach for Detecting Face Mask

Abstract

COVID-19 has made a serious impact throughout the world. Wearing a face mask properly in public is considered an effective strat¬egy to prevent infection. To contribute to community health, this work intends to develop an accurate real-time system for detecting non-mask faces in public. The proposed system can be used by law enforcement authorities to monitor and enforce the proper use of masks. A novel trans¬fer learning-based method to detect face masks efficiently is proposed in this paper. Inspired by EfficientNetV2, a transfer learning model has been proposed to use for detecting whether a person is wearing a mask or not. To our knowledge, the EfficientNetV2 has not been used for detect¬ing face masks. The model building is done based on two standard face mask datasets. The images in the first dataset consist of multiple peo¬ple wearing masks, not wearing masks, and wearing the mask incorrectly. The second dataset consists of masked faces and faces without masks. To validate the generalization capability of the proposed model, the trained model is tested on two new standard datasets. In addition to that, the testing is done on a dataset created by ourselves. The proposed model performs well even on images with distortions such as blurred and noisy images. The model predicts whether the person in the input image is wearing a mask or not and also the correctness of mask-wearing with significantly better accuracy than the existing methods. The explain-ability of the proposed model is explained using a class activation map.