Abstract
This article solves the problem of detecting medical masks on a person's face. Medical mask is one of the most effective measures to prevent infection with COVID-19, and its automatic detection is an actual task. The introduction of automatic recognition of medical masks in existing information security systems will allow quickly identify the violator of the mask regime, which in turn will increase security in a pandemic. The article provides a detailed analysis of existing solutions for face detection and automatic recognition of medical masks, method based on the use of convolutional neural networks was proposed. A distinctive feature of the new method is the use of two neural networks at once, using the RetinaFace neural network architecture at the face search stage and using the Resnet neural network architecture at the face mask recognition stage. It is shown that the use of transfer learning on scales, learned to work with faces, significantly accelerates learning and increases the accuracy of recognition. However, with this approach, there are some false positives, for example, when you try to cover your face with your hands, imitating a medical mask. Based on the study, we can conclude that the algorithm is applicable in the security system to determine the presence/absence of a medical mask on a person's face, as well as the need for additional research to solve the problems of false positives of the algorithm.
Highlights
Nowadays the task of recognition the presence of a medical mask on a person's face has become very relevant in the condition of growing incidence of the new COVID-19 coronavirus infection
Two approaches were prepared: in the first approach, the weights of the ResNet neural network were initialized with weights trained on ImageNet, and the second approach: the weights of the ResNet neural network were initialized with weights trained to recognize faces on the ArcFace metric [17]
The left part of the drawing shows the original image with faces selected in the bounding box – the result of the RetinaFace neural network, the right part of the drawing shows the result of recognizing the presence/absence of a medical mask on the found face with some confidence
Summary
Nowadays the task of recognition the presence of a medical mask on a person's face has become very relevant in the condition of growing incidence of the new COVID-19 coronavirus infection. In [1], the authors solve the problem of face recognition in medical masks. The authors solved the problem of detecting masked faces using a multitasking cascading convolutional neural network (MTCNN). In [2], it is discussed that in recent years, face recognition has become a very difficult task due to various types of occlusion or masks, such as sunglasses, scarves, hats, and various types of makeup or disguise. All this affects the accuracy of facial recognition. Due to the widespread introduction and development of new information technologies, namely neural network approaches, in many areas of human life, there is a new task of automatic detection of a medical mask on a person's face, which will automatically monitor compliance with the mask mode
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More From: International Journal of Advanced Computer Science and Applications
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