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About:
Masked Face Recognition Dataset and Application
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wasabi.inria.fr
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research paper
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Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
Masked Face Recognition Dataset and Application
Creator
Wu, Hao
Hong, Qi
Miao, Yu
Chen, Heling
Huang, Baojin
Huang, Zhibing
Jiang, Kui
Liang, Jinbi
Pei, Yingjiao
Wang, Guangcheng
Wang, Nanxi
Wang, Zhongyuan
Xiong, Zhangyang
Yi, Peng
Source
ArXiv
abstract
In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are 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 Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset.
has issue date
2020-03-20
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arxiv
sha1sum (hex)
9e3a9bddd773cd34b186cbd3489a112598583294
resource representing a document's title
Masked Face Recognition Dataset and Application
resource representing a document's body
covid:9e3a9bddd773cd34b186cbd3489a112598583294#body_text
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