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- W3212323791 abstract "In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This nearly makes conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, community visit check-in, etc. Therefore, it is very urgent to boost performance of existing face recognition systems on masked faces. Most current advanced face recognition approaches are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset." @default.
- W3212323791 created "2021-11-22" @default.
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- W3212323791 date "2021-10-01" @default.
- W3212323791 modified "2023-09-26" @default.
- W3212323791 title "Masked Face Recognition Datasets and Validation" @default.
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- W3212323791 doi "https://doi.org/10.1109/iccvw54120.2021.00172" @default.
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