  Estimating the mask-wearing ratio in public places is important as it enables
health authorities to promptly analyze and implement policies. Methods for
estimating the mask-wearing ratio on the basis of image analysis have been
reported. However, there is still a lack of comprehensive research on both
methodologies and datasets. Most recent reports straightforwardly propose
estimating the ratio by applying conventional object detection and
classification methods. It is feasible to use regression-based approaches to
estimate the number of people wearing masks, especially for congested scenes
with tiny and occluded faces, but this has not been well studied. A large-scale
and well-annotated dataset is still in demand. In this paper, we present two
methods for ratio estimation that leverage either a detection-based or
regression-based approach. For the detection-based approach, we improved the
state-of-the-art face detector, RetinaFace, used to estimate the ratio. For the
regression-based approach, we fine-tuned the baseline network, CSRNet, used to
estimate the density maps for masked and unmasked faces. We also present the
first large-scale dataset, the ``NFM dataset,'' which contains 581,108 face
annotations extracted from 18,088 video frames in 17 street-view videos.
Experiments demonstrated that the RetinaFace-based method has higher accuracy
under various situations and that the CSRNet-based method has a shorter
operation time thanks to its compactness.
