A Survey on Masked Facial Detection Methods and Datasets for Fighting
Against COVID-19
Abstract
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 handcrafted 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.