During the COVID-19 pandemic, wearing a face mask has been known to be an
effective way to prevent the spread of COVID-19. In lots of monitoring tasks,
humans have been replaced with computers thanks to the outstanding performance
of the deep learning models. Monitoring the wearing of a face mask is another
task that can be done by deep learning models with acceptable accuracy. The
main challenge of this task is the limited amount of data because of the
quarantine. In this paper, we did an investigation on the capability of three
state-of-the-art object detection neural networks on face mask detection for
real-time applications. As mentioned, here are three models used, Single Shot
Detector (SSD), two versions of You Only Look Once (YOLO) i.e., YOLOv4-tiny,
and YOLOv4-tiny-3l from which the best was selected. In the proposed method,
according to the performance of different models, the best model that can be
suitable for use in real-world and mobile device applications in comparison to
other recent studies was the YOLOv4-tiny model, with 85.31% and 50.66 for mean
Average Precision (mAP) and Frames Per Second (FPS), respectively. These
acceptable values were achieved using two datasets with only 1531 images in
three separate classes.

本文探讨了使用三种最先进的目标检测神经网络在实时应用中用于口罩检测的能力，并在只有 1531 张图片的三个单独的类别的数据集上，通过使用 YOLOv4-tiny 模型获得了 85.31% 和 50.66 的平均精度和秒数，分别是其他最近研究的最佳模型。

基于深度学习的 COVID-19 疫情期间佩戴口罩检测

Wearing face mask detection using deep learning through COVID-19  pandemic

An outbreak of the coronavirus disease which occurred three years later and
it has hit the world again with many evolutions. The effects on the human race
have already been profound. We can only safeguard ourselves against this
pandemic by mandating a "Face Mask" also maintaining the "Social Distancing."
The necessity of protective face masks in all gatherings is required by many
civil institutions in India. As a result of the substantial human resource
utilization, personally examining the whole country with a huge population like
India, to determine whether the execution of mask wearing and social distance
maintained is unfeasible. The COVID-19 Social Distancing Detector System is a
single-stage detector that employs deep learning to integrate high-end semantic
data to a CNN module in order to maintain social distances and simultaneously
monitor violations within a specified region. By deploying current Security
footages, CCTV cameras, and computer vision (CV), it will also be able to
identify those who are experiencing the calamity of social separation.
Providing tools for safety and security, this technology disposes the need for
a labor-force based surveillance system, yet a manual governing body is still
required to monitor, track, and inform on the violations that are committed.
Any sort of infrastructure, including universities, hospitals, offices of the
government, schools, and building sites, can employ the technology. Therefore,
the risk management system created to report and analyze video streams along
with the social distance detector system might help to ensure our protection
and security as well as the security of our loved ones. Furthermore, we will
discuss about deployment and improvement of the project overall.

使用深度学习和计算机视觉技术开发的 COVID-19 Social Distancing Detector System 可在指定区域内维持社交距离和监测违规行为，可用于任何场所，并可提高风险管理和保护个人安全。

使用深度学习和风险管理系统进行社交距离检测

Social Distance Detection Using Deep Learning And Risk Management System

Facial expression recognition (FER) algorithms work well in constrained
environments with little or no occlusion of the face. However, real-world face
occlusion is prevalent, most notably with the need to use a face mask in the
current Covid-19 scenario. While there are works on the problem of occlusion in
FER, little has been done before on the particular face mask scenario.
Moreover, the few works in this area largely use synthetically created masked
FER datasets. Motivated by these challenges posed by the pandemic to FER, we
present a novel dataset, the Masked Student Dataset of Expressions or MSD-E,
consisting of 1,960 real-world non-masked and masked facial expression images
collected from 142 individuals. Along with the issue of obfuscated facial
features, we illustrate how other subtler issues in masked FER are represented
in our dataset. We then provide baseline results using ResNet-18, finding that
its performance dips in the non-masked case when trained for FER in the
presence of masks. To tackle this, we test two training paradigms: contrastive
learning and knowledge distillation, and find that they increase the model's
performance in the masked scenario while maintaining its non-masked
performance. We further visualise our results using t-SNE plots and Grad-CAM,
demonstrating that these paradigms capitalise on the limited features available
in the masked scenario. Finally, we benchmark SOTA methods on MSD-E.

本研究针对 Covid-19 情况下面罩对面部表情识别的影响，提出了一个新的真实世界数据集 MSD-E，并通过对比学习和知识蒸馏的方式增加了模型在面罩环境下的表现，最终在该数据集上进行了性能基准测试。

表情遮蔽学生数据集

Masked Student Dataset of Expressions

Using the face as a biometric identity trait is motivated by the contactless
nature of the capture process and the high accuracy of the recognition
algorithms. After the current COVID-19 pandemic, wearing a face mask has been
imposed in public places to keep the pandemic under control. However, face
occlusion due to wearing a mask presents an emerging challenge for face
recognition systems. In this paper, we present a solution to improve masked
face recognition performance. Specifically, we propose the Embedding Unmasking
Model (EUM) operated on top of existing face recognition models. We also
propose a novel loss function, the Self-restrained Triplet (SRT), which enabled
the EUM to produce embeddings similar to these of unmasked faces of the same
identities. The achieved evaluation results on three face recognition models,
two real masked datasets, and two synthetically generated masked face datasets
proved that our proposed approach significantly improves the performance in
most experimental settings.

该论文提出了一种名为 EUM 的嵌入去除模型和一种新的损失函数 SRT，可显著改善大多数实验设置中面部识别系统中戴口罩的面部识别问题。