RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
; : 832-837, 2021.
Article
in English
| Scopus | ID: covidwho-1706842
ABSTRACT
Coronavirus 2019 has made a significant impact on the world. One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. In this paper, we proposed RetinaFaceMask, the first high-performance single stage face mask detector. First, to solve the issue that existing studies did not distinguish between correct and incorrect mask wearing states, we established a new dataset containing these annotations. Second, we proposed a context attention module to focus on learning discriminated features associated with face mask wearing states. Third, we transferred the knowledge from the face detection task, inspired by how humans improve their ability via learning from similar tasks. Ablation studies showed the advantages of the proposed model. Experimental findings on both the public and new datasets demonstrated the state-of-the-art performance of our model. © 2021 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Year:
2021
Document Type:
Article
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