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%.

本文报道了使用热成像技术可以检测口罩佩戴的位置和类型，从而解决新冠病毒传播的问题，针对不同的深度学习模型，本文提出了一种 Yolov5 模型及一个基于卷积神经网络的自编码器分类器，准确率分别达到 97％和 91％。

热成像人脸图像的口罩检测和分类

Mask Detection and Classification in Thermal Face Images

With the recent world-wide COVID-19 pandemic, using face masks have become an
important part of our lives. People are encouraged to cover their faces when in
public area to avoid the spread of infection. The use of these face masks has
raised a serious question on the accuracy of the facial recognition system used
for tracking school/office attendance and to unlock phones. Many organizations
use facial recognition as a means of authentication and have already developed
the necessary datasets in-house to be able to deploy such a system.
Unfortunately, masked faces make it difficult to be detected and recognized,
thereby threatening to make the in-house datasets invalid and making such
facial recognition systems inoperable. This paper addresses a methodology to
use the current facial datasets by augmenting it with tools that enable masked
faces to be recognized with low false-positive rates and high overall accuracy,
without requiring the user dataset to be recreated by taking new pictures for
authentication. We present an open-source tool, MaskTheFace to mask faces
effectively creating a large dataset of masked faces. The dataset generated
with this tool is then used towards training an effective facial recognition
system with target accuracy for masked faces. We report an increase of 38% in
the true positive rate for the Facenet system. We also test the accuracy of
re-trained system on a custom real-world dataset MFR2 and report similar
accuracy.

本文提出了一种使用实时口罩检测来增强人脸识别系统的现有数据集的方法，并利用 MaskTheFace 工具创建大量实时口罩数据集以及使用处理后的数据集提高了 FACENET 及在实际数据集 MFR2 上的识别率。