Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse state-of-theart FR models. Our analysis on the Balanced Faces in the Wild (BFW) dataset shows that all techniques considered are affected more by variations in race than sex. While the general-purpose image quality assessment methods appear to be less biased with respect to the two demographic factors considered, the supervised and unsupervised face image quality assessment methods both show strong bias with a tendency to favor white individuals (of either sex). In addition, we found that methods that are less racially biased perform worse overall. This suggests that the observed bias in FIQA methods is to a significant extent related to the underlying face recognition system.

翻译：面部图像质量评估(FIQA)试图通过提供有关抽样质量的额外信息来改善面部识别(FIQA)绩效。由于FIQA方法试图估计样本对面部识别的效用,因此有理由认为这些方法受到基本面部识别系统的重大影响。虽然现代面部识别系统表现良好,但一些研究发现,这类系统往往存在人口偏差问题,因此,这类问题也出现在FIQA技术中。为了调查与FIQA方法有关的人口偏差,本文件介绍了一项综合研究,涉及各种质量评估方法(一般目的图像质量评估、监督面部质量评估和不受监督的面部质量评估方法)和三种不同的FR模型。我们对野生(BFW)中平衡面部的分析表明,所有考虑的技术都受到种族差异而非性别差异的影响。虽然一般用途图像质量评估方法似乎与所考虑的两种人口因素相比不那么有偏见,但受监督和未经监督的面部位图像质量评估方法都显示出强烈的偏向性,同时表现出偏向偏向整体偏向。我们所观察到的性别偏向更深。