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.

本文对现代人脸检测算法面临的挑战进行了阐述，着重研究了一类子问题：检测戴口罩的人脸。文章回顾了当前的最先进人脸检测器和专门针对戴口罩人脸识别的算法，并评估了一组代表性的检测器在检测戴口罩人脸问题上的表现，并讨论了影响表现的可能因素。

口罩人脸检测算法的比较研究

A Comparative Study of Face Detection Algorithms for Masked Face  Detection

In this study of the face recognition on masked versus unmasked faces
generated using Flickr-Faces-HQ and SpeakingFaces datasets, we report 36.78%
degradation of recognition performance caused by the mask-wearing at the time
of pandemics, in particular, in border checkpoint scenarios. We have achieved
better performance and reduced the degradation to 1.79% using advanced deep
learning approaches in the cross-spectral domain.

本研究使用 Flickr-Faces-HQ 和 SpeakingFaces 数据集对戴口罩与不戴口罩的人脸识别进行比较，发现在疫情边境检查场景下，口罩会导致 36.78％的性能下降，但使用跨光谱深度学习方法可将性能下降降至 1.79％。