000118166 001__ 118166
000118166 005__ 20230519145607.0
000118166 0247_ $$2doi$$a10.3390/su13126900
000118166 0248_ $$2sideral$$a127240
000118166 037__ $$aART-2021-127240
000118166 041__ $$aeng
000118166 100__ $$aTalahua J.S.
000118166 245__ $$aFacial recognition system for people with and without face mask in times of the covid-19 pandemic
000118166 260__ $$c2021
000118166 5060_ $$aAccess copy available to the general public$$fUnrestricted
000118166 5203_ $$aIn the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv''s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13, 359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
000118166 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000118166 592__ $$a0.664$$b2021
000118166 590__ $$a3.889$$b2021
000118166 593__ $$aEnergy Engineering and Power Technology$$c2021$$dQ1
000118166 591__ $$aENVIRONMENTAL STUDIES$$b57 / 128 = 0.445$$c2021$$dQ2$$eT2
000118166 593__ $$aRenewable Energy, Sustainability and the Environment$$c2021$$dQ1
000118166 591__ $$aENVIRONMENTAL SCIENCES$$b133 / 279 = 0.477$$c2021$$dQ2$$eT2
000118166 593__ $$aManagement, Monitoring, Policy and Law$$c2021$$dQ1
000118166 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b35 / 47 = 0.745$$c2021$$dQ3$$eT3
000118166 593__ $$aGeography, Planning and Development$$c2021$$dQ1
000118166 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b7 / 9 = 0.778$$c2021$$dQ4$$eT3
000118166 594__ $$a5.0$$b2021
000118166 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000118166 700__ $$aBuele J.
000118166 700__ $$aCalvopina P.
000118166 700__ $$aVarela-Aldas J.
000118166 773__ $$g13, 12 (2021), 6900 [19 pp]$$pSustainability (Basel)$$tSustainability (Switzerland)$$x2071-1050
000118166 8564_ $$s3198111$$uhttps://zaguan.unizar.es/record/118166/files/texto_completo.pdf$$yVersión publicada
000118166 8564_ $$s2798828$$uhttps://zaguan.unizar.es/record/118166/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000118166 909CO $$ooai:zaguan.unizar.es:118166$$particulos$$pdriver
000118166 951__ $$a2023-05-18-16:05:31
000118166 980__ $$aARTICLE