  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.
