  In the absence of vaccines or medicines to stop COVID-19, one of the
effective methods to slow the spread of the coronavirus and reduce the
overloading of healthcare is to wear a face mask. Nevertheless, to mandate the
use of face masks or coverings in public areas, additional human resources are
required, which is tedious and attention-intensive. To automate the monitoring
process, one of the promising solutions is to leverage existing object
detection models to detect the faces with or without masks. As such, security
officers do not have to stare at the monitoring devices or crowds, and only
have to deal with the alerts triggered by the detection of faces without masks.
Existing object detection models usually focus on designing the CNN-based
network architectures for extracting discriminative features. However, the size
of training datasets of face mask detection is small, while the difference
between faces with and without masks is subtle. Therefore, in this paper, we
propose a face mask detection framework that uses the context attention module
to enable the effective attention of the feed-forward convolution neural
network by adapting their attention maps feature refinement. Moreover, we
further propose an anchor-free detector with Triplet-Consistency Representation
Learning by integrating the consistency loss and the triplet loss to deal with
the small-scale training data and the similarity between masks and occlusions.
Extensive experimental results show that our method outperforms the other
state-of-the-art methods. The source code is released as a public download to
improve public health at https://github.com/wei-1006/MaskFaceDetection.
