  Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to
the world since its outbreak. To fight against the disease, a series of
artificial intelligence (AI) techniques are developed and applied to real-world
scenarios such as safety monitoring, disease diagnosis, infection risk
assessment, lesion segmentation of COVID-19 CT scans,etc. The coronavirus
epidemics have forced people wear masks to counteract the transmission of
virus, which also brings difficulties to monitor large groups of people wearing
masks. In this paper, we primarily focus on the AI techniques of masked facial
detection and related datasets. We survey the recent advances, beginning with
the descriptions of masked facial detection datasets. Thirteen available
datasets are described and discussed in details. Then, the methods are roughly
categorized into two classes: conventional methods and neural network-based
methods. Conventional methods are usually trained by boosting algorithms with
hand-crafted features, which accounts for a small proportion. Neural
network-based methods are further classified as three parts according to the
number of processing stages. Representative algorithms are described in detail,
coupled with some typical techniques that are described briefly. Finally, we
summarize the recent benchmarking results, give the discussions on the
limitations of datasets and methods, and expand future research directions. To
our knowledge, this is the first survey about masked facial detection methods
and datasets. Hopefully our survey could provide some help to fight against
epidemics.
