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Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning

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Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning

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dc.contributor.author Tomás Gironés, Jesús es_ES
dc.contributor.author REGO MAÑEZ, ALBERT es_ES
dc.contributor.author Viciano-Tudela, Sandra es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2022-10-21T18:03:12Z
dc.date.available 2022-10-21T18:03:12Z
dc.date.issued 2021-08 es_ES
dc.identifier.uri http://hdl.handle.net/10251/188552
dc.description.abstract [EN] 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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Healthcare es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Facemask-wearing condition es_ES
dc.subject Transfer learning es_ES
dc.subject Convolutional neural network es_ES
dc.subject Deep learning es_ES
dc.subject Facial recognition es_ES
dc.subject COVID-19 es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/healthcare9081050 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Tomás Gironés, J.; Rego Mañez, A.; Viciano-Tudela, S.; Lloret, J. (2021). Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning. Healthcare. 9(8):1-17. https://doi.org/10.3390/healthcare9081050 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/healthcare9081050 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 8 es_ES
dc.identifier.eissn 2227-9032 es_ES
dc.identifier.pmid 34442187 es_ES
dc.identifier.pmcid PMC8391571 es_ES
dc.relation.pasarela S\473274 es_ES
dc.contributor.funder Universitat Politècnica de València
upv.costeAPC 1700 es_ES


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