Abstract
Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthiness of FRS, more recent efforts have tried to find the causes. However, these studies make use of manually selected, correlated, and small-sized sets of facial features to support their claims. In this work, we analyse gender bias in face recognition by successfully extending the search domain to decorrelated combinations of 40 non-demographic facial characteristics. First, we propose a toolchain to effectively decorrelate and aggregate facial attributes to enable a less-biased gender analysis on large-scale data. Second, we introduce two new fairness metrics to measure fairness with and without context. Based on these grounds, we thirdly present a novel unsupervised algorithm able to reliably identify attribute combinations that lead to vanishing bias when used as filter predicates for balanced testing datasets. The experiments show that the gender gap vanishes when images of male and female subjects share specific attributes, clearly indicating that the issue is not a question of biology but of the social definition of appearance. These findings could reshape our understanding of fairness in face biometrics and provide insights into FRS, helping to address gender bias issues.
Abstract (translated)
面部识别系统（FRS）在不同性别用户之间的准确度存在显著差异。这种性别差距降低了FRS的可信度，因此最近的研究努力尝试找出原因。然而，这些研究使用了手动挑选的小规模且相关联的人脸特征集来支持其论点。在这项工作中，我们通过将搜索领域扩展到40种非人口统计学面部特征的相关组合分析了面部识别中的性别偏见。 首先，我们提出了一套工具链，能够有效地去相关和聚合面部属性，以实现对大规模数据的较少偏向性性别分析。其次，我们引入了两个新的公平度量指标来衡量有无上下文情况下的公平性。基于这些基础，第三，我们提出了一个新颖的无监督算法，该算法可以可靠地识别出在平衡测试数据集中用作过滤条件时导致偏见消失的属性组合。 实验表明，当男性和女性的照片具有特定共同特征时，性别差距就会消失，这清楚地表明问题不在于生物学上的差异，而在于外貌的社会定义。这些发现可能会重塑我们对面部生物识别公平性的理解，并为解决面部识别系统中的性别偏见提供见解。
URL
https://arxiv.org/abs/2501.12020
