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Coronavirus 2019 has made a significant impact on the world. One effectivestrategy to prevent infection for people is to wear masks in public places.Certain public service providers require clients to use their services only ifthey properly wear masks. There are, however, only a few research studies onautomatic face mask detection. In this paper, we proposed RetinaFaceMask, thefirst high-performance single stage face mask detector. First, to solve theissue that existing studies did not distinguish between correct and incorrectmask wearing states, we established a new dataset containing these annotations.Second, we proposed a context attention module to focus on learningdiscriminated features associated with face mask wearing states. Third, wetransferred the knowledge from the face detection task, inspired by how humansimprove their ability via learning from similar tasks. Ablation studies showedthe advantages of the proposed model. Experimental findings on both the publicand new datasets demonstrated the state-of-the-art performance of our model.

RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic