Deep learning-based person identification and verification systems have
remarkably improved in terms of accuracy in recent years; however, such
systems, including widely popular cloud-based solutions, have been found to
exhibit significant biases related to race, age, and gender, a problem that
requires in-depth exploration and solutions. This paper presents an in-depth
analysis, with a particular emphasis on the intersectionality of these
demographic factors. Intersectional bias refers to the performance
discrepancies w.r.t. the different combinations of race, age, and gender
groups, an area relatively unexplored in current literature. Furthermore, the
reliance of most state-of-the-art approaches on accuracy as the principal
evaluation metric often masks significant demographic disparities in
performance. To counter this crucial limitation, we incorporate five additional
metrics in our quantitative analysis, including disparate impact and
mistreatment metrics, which are typically ignored by the relevant
fairness-aware approaches. Results on the Racial Faces in-the-Wild (RFW)
benchmark indicate pervasive biases in face recognition systems, extending
beyond race, with different demographic factors yielding significantly
disparate outcomes. In particular, Africans demonstrate an 11.25% lower True
Positive Rate (TPR) compared to Caucasians, while only a 3.51% accuracy drop is
observed. Even more concerning, the intersections of multiple protected groups,
such as African females over 60 years old, demonstrate a +39.89% disparate
mistreatment rate compared to the highest Caucasians rate. By shedding light on
these biases and their implications, this paper aims to stimulate further
research towards developing fairer, more equitable face recognition and
verification systems.

通过深入分析，本文重点讨论了涉及种族、年龄和性别等因素的交叉偏见问题，提出了在当前文献中较少探索的种族、年龄和性别组合方面的性能差异。此外，本文在定量分析中引入了五个补充指标，包括不公平影响和不公平待遇指标，以克服当前主流方法中常被忽视的重要局限。研究结果表明，人脸识别系统存在普遍偏见，不仅限于种族，不同的人口统计因素导致显著不同的结果。通过揭示这些偏见及其影响，本文旨在促进进一步研究，以开发更公正、更公平的人脸识别和验证系统。