Abstract
The COVID-19 pandemic has been a worldwide catastrophe. Its impact, not only economically, but also socially and in terms of human lives, was unexpected. Each of the many mechanisms to fight the contagiousness of the illness has been proven to be extremely important. One of the most important mechanisms is the use of facemasks. However, the wearing the facemasks incorrectly makes this prevention method useless. Artificial Intelligence (AI) and especially facial recognition techniques can be used to detect misuses and reduce virus transmission, especially indoors. In this paper, we present an intelligent method to automatically detect when facemasks are being worn incorrectly in real-time scenarios. Our proposal uses Convolutional Neural Networks (CNN) with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading. The main problem that we have detected is that there is currently no training set for this task. It is for this reason that we have requested the participation of citizens by taking different selfies through an app and placing the mask in different positions. Thus, we have been able to solve this problem. The results show that the accuracy achieved with transfer learning slightly improves the accuracy achieved with convolutional neural networks. Finally, we have also developed an Android-app demo that validates the proposal in real scenarios.
Highlights
This study was conducted on exhaled air samples of SARS-CoV and MERS-CoV from infected patients and the findings indicate that surgical masks effectively reduce the emission of viral particles [9]
The current pandemic situation produced by SARS-CoV-2, in which we find ourselves, has led various authors to try to improve the control of the spread of the virus by developing applications to control the use of masks
Transfer learning was applied for fine-tuning the same parameters, except the learning rate reduced to 10–5
Summary
In December 2019, cases of pneumonia of unknown origin appeared in Wuhan, Hubei, China, the clinical symptoms of which were very similar to pneumonia of viral origin. In the first group of infected people, it was determined that it was a zoonotic infection, that is, a viral transmission from animals to humans [1]. Due to the taking of samples from the lower respiratory tract, employing the RT-PCR technique in real-time, genome sequencing was carried out. This fact allowed us to clarify the taxonomy of this virus. New Coronavirus 2019 (2019-nCoV) was the name that was assigned to it
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