Masked Face Recognition Dataset and Application

By Zhongyuan Wang, Guangcheng Wang, Baojin Huang, Zhangyang Xiong, Q. Hong, Hao Wu, Peng Yi, Kui Jiang, Nanxi Wang, Yingjiao Pei and 4 more. arXiv.org, 2020

471 citations recorded by the source corpus.

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: this https URL.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9e3a9bddd773cd34b186cbd3489a112598583294

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Citations in the corpus

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2 (2020, methodology): “An accuracy of over 90 % was obtained from Minivision Technology (Wang et al., 2020).”
A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic (2020, unknown): “The author of RMFD created one of the biggest masked face datasets used in this research.”
Past, Present, and Future of Face Recognition: A Review (2020, background): “[45] proposed three types of masked face datasets, which are:”