Abstract
Demographic bias in high-performance face recognition (FR) systems often eludes detection by existing metrics, especially with respect to subtle disparities in the tails of the score distribution. We introduce the Comprehensive Equity Index (CEI), a novel metric designed to address this limitation. CEI uniquely analyzes genuine and impostor score distributions separately, enabling a configurable focus on tail probabilities while also considering overall distribution shapes. Our extensive experiments (evaluating state-of-the-art FR systems, intentionally biased models, and diverse datasets) confirm CEI's superior ability to detect nuanced biases where previous methods fall short. Furthermore, we present CEI^A, an automated version of the metric that enhances objectivity and simplifies practical application. CEI provides a robust and sensitive tool for operational FR fairness assessment. The proposed methods have been developed particularly for bias evaluation in face biometrics but, in general, they are applicable for comparing statistical distributions in any problem where one is interested in analyzing the distribution tails.
Abstract (translated)
高性能面部识别（FR）系统中的人口统计偏差往往被现有的度量标准所忽略，特别是在得分分布尾部的细微差异方面。我们引入了全面公平指数（Comprehensive Equity Index, CEI），这是一种旨在解决此限制的新颖度量标准。CEI独特地分别分析真实和冒名顶替者的分数分布，并允许对尾概率进行可配置的重点关注，同时考虑整体分布形态。我们的广泛实验（评估最先进的FR系统、故意带有偏见的模型以及多样化的数据集）证实了CEI在检测先前方法未能发现的细微偏差方面的卓越能力。此外，我们还提出了CEI^A，即该度量标准的自动化版本，它增强了客观性并简化了实际应用。CEI为操作层面的FR公平性评估提供了一个稳健且敏感的工具。所提出的方法特别为了面部生物识别中的偏见评估而开发，但一般来说，在任何关注分布尾部的统计分析问题中都适用。
URL
https://arxiv.org/abs/2506.10564
