Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics

• URL: http://arxiv.org/abs/2506.10564v1
• Date: Thu, 12 Jun 2025 10:43:31 GMT
• Title: Balancing Tails when Comparing Distributions: Comprehensive Equity Index (CEI) with Application to Bias Evaluation in Operational Face Biometrics
• Authors: Imanol Solano, Julian Fierrez, Aythami Morales, Alejandro Peña, Ruben Tolosana, Francisco Zamora-Martinez, Javier San Agustin,
• Abstract summary: Comprehensive Equity Index (CEI) is a novel metric designed to detect demographic bias in face recognition systems.<n>Our experiments confirm CEI's superior ability to detect nuanced biases where previous methods fall short.<n>CEI provides a robust and sensitive tool for operational fairness assessment.
• Score: 47.762333925222926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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