RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic
This addresses the need for automated mask-wearing compliance checks in public services during the pandemic, but it is incremental as it builds on existing face detection methods.
The authors tackled the problem of automatically detecting whether people are wearing face masks correctly to help control COVID-19, and their RetinaFaceMask model achieved state-of-the-art performance on new and public datasets.
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.