  Contemporary face detection algorithms have to deal with many challenges such
as variations in pose, illumination, and scale. A subclass of the face
detection problem that has recently gained increasing attention is occluded
face detection, or more specifically, the detection of masked faces. Three
years on since the advent of the COVID-19 pandemic, there is still a complete
lack of evidence regarding how well existing face detection algorithms perform
on masked faces. This article first offers a brief review of state-of-the-art
face detectors and detectors made for the masked face problem, along with a
review of the existing masked face datasets. We evaluate and compare the
performances of a well-representative set of face detectors at masked face
detection and conclude with a discussion on the possible contributing factors
to their performance.
