Campos Alexis, Melin Patricia, Sánchez Daniela
Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico.
Life (Basel). 2023 Jan 29;13(2):368. doi: 10.3390/life13020368.
The world has been greatly affected by the COVID-19 pandemic, causing people to remain isolated and decreasing the interaction between people. Accordingly, various measures have been taken to continue with a new normal way of life, which is why there is a need to implement the use of technologies and systems to decrease the spread of the virus. This research proposes a real-time system to identify the region of the face using preprocessing techniques and then classify the people who are using the mask, through a new convolutional neural network (CNN) model. The approach considers three different classes, assigning a different color to identify the corresponding class: green for persons using the mask correctly, yellow when used incorrectly, and red when people do not have a mask. This study validates that CNN models can be very effective in carrying out these types of tasks, identifying faces, and classifying them according to the class. The real-time system is developed using a Raspberry Pi 4, which can be used for the monitoring and alarm of humans who do not use the mask. This study mainly benefits society by decreasing the spread of the virus between people. The proposed model achieves 99.69% accuracy with the MaskedFace-Net dataset, which is very good when compared to other works in the current literature.
新冠疫情给世界带来了巨大影响，导致人们保持隔离状态，人与人之间的互动减少。因此，人们采取了各种措施来维持新的正常生活方式，这就是为什么需要采用技术和系统来减少病毒传播。本研究提出了一种实时系统，该系统使用预处理技术识别面部区域，然后通过一种新的卷积神经网络（CNN）模型对佩戴口罩的人进行分类。该方法考虑了三个不同的类别，为每个类别分配一种不同的颜色来进行识别：绿色表示正确佩戴口罩的人，黄色表示佩戴不正确，红色表示未佩戴口罩的人。本研究验证了CNN模型在执行这类任务、识别面部并根据类别进行分类方面非常有效。该实时系统是使用树莓派4开发的，可用于对未佩戴口罩的人员进行监控和报警。本研究主要通过减少人与人之间病毒的传播而造福社会。所提出的模型在MaskedFace-Net数据集上达到了99.69%的准确率，与当前文献中的其他研究相比，这是非常不错的。
