An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics
CEUR Workshop Proceedings
; 3398:36-41, 2022.
Artículo
en Inglés
| Scopus | ID: covidwho-20234692
ABSTRACT
The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.
CNN; COVID-19; Face Mask; ResNet50; Computer viruses; Convolutional neural networks; Face recognition; Signal detection; Surgery; Viruses; Convolutional neural network; Detection algorithm; Face masks; Facial recognition; Health care professionals; Infection control; Medical settings; Network-based; Preventive measures
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Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudio experimental
Idioma:
Inglés
Revista:
CEUR Workshop Proceedings
Año:
2022
Tipo del documento:
Artículo
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