Detection of Face Mask in Thermal Images Using Deep CNN

Abstract

Coronavirus (COVID-19) is a major health crisis across the globe, and one of the effective methods against the transmission of virus is wearing a face mask. It has become extremely important to monitor if people are wearing face mask when moving out. The aim of project is to automate the detection of face mask using images captured from a thermal camera. The problem is posed as a binary classification problem, and the input face image is classified as with mask or without mask. Transfer learning is used for classification, wherein deep CNN model, MobileNetV2, is used as a base model for feature extraction. A dataset of face images with and without mask is prepared using lepton FLIR camera interfaced to a Raspberry Pi board. The built model is able to detect people who are wearing a face mask and not wearing with an accuracy of 98%.