Abstract
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.
Abstract (translated)
当代人脸识别算法必须处理许多挑战，例如姿势、照明和大小的变化。最近越来越引人关注的是遮挡人脸识别，或更具体地说，是识别口罩面容的问题。自 COVID-19 大流行开始以来已经三年了，但仍然存在没有任何证据表明现有人脸识别算法在口罩面容识别方面表现如何的问题。本文首先简要介绍了为口罩面容问题开发的最先进的人脸识别算法和算法，并回顾了现有的口罩面容数据集。我们评估和比较了一组代表性的人脸识别算法在口罩面容识别方面的性能，并最后讨论了可能影响其性能的可能影响因素。
URL
https://arxiv.org/abs/2305.11077
