Mask Detection and Classification in Thermal Face Images

TL;DR

This paper explores thermal imaging combined with deep learning models to detect, localize, and classify face masks, achieving high accuracy and extending existing datasets for improved mask detection in health-related contexts.

Contribution

It introduces an extended thermal image dataset with annotations and evaluates deep learning models, notably Yolov5 and a CNN autoencoder, for mask detection and classification.

Findings

Yolov5 nano achieved over 97% mAP in mask detection.

CNN autoencoder-based classifier reached 91% accuracy in mask type classification.

Thermal imaging is effective for mask detection and classification tasks.

Abstract

Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the possibility of detecting (localizing) a mask on the face, as well as to check whether it is possible to classify the type of mask on the face. The previously proposed dataset of thermal images was extended and annotated with the description of a type of mask and a location of a mask within a face. Different deep learning models were adapted. The best model for face mask detection turned out to be the Yolov5 model in the "nano" version, reaching mAP higher than 97% and precision of about 95%. High accuracy was also obtained for mask type classification. The best results were obtained for the convolutional neural network model built on an autoencoder initially trained in the thermal image reconstruction problem. The pretrained encoder was used to train a classifier which achieved an accuracy of 91%.