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.

翻译：当代人脸检测算法需应对姿态、光照及尺度变化等多重挑战。近年来，人脸检测领域的一个子问题——遮挡人脸检测，尤其是口罩佩戴人脸的检测，日益受到关注。尽管新冠疫情已持续三年有余，但现有主流人脸检测算法在口罩遮挡场景下的实际表现尚未有系统性研究。本文首先综述了当前先进的人脸检测器以及面向口罩人脸问题的专用检测器，同时回顾了现有口罩人脸数据集。我们选取一组具有代表性的人脸检测器，对其在口罩人脸检测任务中的性能进行系统评估与对比，最后探讨了影响算法性能的潜在因素。