{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:12:57Z","timestamp":1778346777897,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bingtuan Science and Technology Program","award":["2019BC008"],"award-info":[{"award-number":["2019BC008"]}]},{"name":"Bingtuan Science and Technology Program","award":["2022DB005"],"award-info":[{"award-number":["2022DB005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recognition model and proposes an improvement of Facenet. We use ConvNeXt-T as the backbone of the network model and add the ECA (Efficient Channel Attention) mechanism. This enhances the feature extraction of the unobscured part of the face to obtain more useful information, while avoiding dimensionality reduction and not increasing the model complexity. We design new face recognition models by investigating the effects of different attention mechanisms on face mask recognition models and the effects of different data set ratios on experimental results. In addition, we construct a large set of faces wearing masks so that we can efficiently and quickly train the model. Through experiments, our model proved to be 99.76% accurate for real faces wearing masks. A combined accuracy of 99.48% for extreme environments such as too high or lousy contrast and brightness.<\/jats:p>","DOI":"10.3390\/info14020087","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T03:36:57Z","timestamp":1675395417000},"page":"87","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Masked Face Recognition System Based on Attention Mechanism"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuming","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3641-2686","authenticated-orcid":false,"given":"Hua","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer Sciences, Wuhan University, Wuhan 430010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111099","DOI":"10.1016\/j.envres.2021.111099","article-title":"The impact of first and second wave of the COVID-19 pandemic in society: Comparative analysis to support control measures to cope with negative effects of future infectious diseases","volume":"197","author":"Coccia","year":"2021","journal-title":"Environ. 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