ArticleJournal of the American Medical Informatics Association : JAMIA2021
Transfer learning for mobile real-time face mask detection and localization.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 53 citations in OpenAlex.
- A multi-class framework for face mask compliance detection using lightweight deep learning models.Scientific reports · 2026Article
- Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation.Sensors (Basel, Switzerland) · 2023Article
- IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection.Heliyon · 2023Article
- Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19.Applied soft computing · 2022Article
- Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism.Computational intelligence and neuroscience · 2022Review
- EfficientMask-Net for face authentication in the era of COVID-19 pandemic.Signal, image and video processing · 2022Article
- Biomedical and health informatics continue to contribute to COVID-19 pandemic solutions and beyond.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDue to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS AND
methodsFor these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability.
resultsTo asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION AND
conclusionThe experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.