Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics

TL;DR

This paper introduces the Comprehensive Equity Index (CEI), a new metric for quantifying demographic biases in biometric face recognition systems by combining distribution shape and tail differences, validated on real-world datasets.

Contribution

The paper proposes the CEI, a novel bias metric that improves bias detection in face recognition by integrating error rate and distribution shape analysis, addressing limitations of existing metrics.

Findings

CEI effectively detects biases in face recognition systems.

CEI outperforms existing metrics on real-world datasets.

The metric is validated on high-performance models and diverse databases.

Abstract

We present a novel metric designed, among other applications, to quantify biased behaviors of machine learning models. As its core, the metric consists of a new similarity metric between score distributions that balances both their general shapes and tails' probabilities. In that sense, our proposed metric may be useful in many application areas. Here we focus on and apply it to the operational evaluation of face recognition systems, with special attention to quantifying demographic biases; an application where our metric is especially useful. The topic of demographic bias and fairness in biometric recognition systems has gained major attention in recent years. The usage of these systems has spread in society, raising concerns about the extent to which these systems treat different population groups. A relevant step to prevent and mitigate demographic biases is first to detect and quantify them. Traditionally, two approaches have been studied to quantify differences between population groups in machine learning literature: 1) measuring differences in error rates, and 2) measuring differences in recognition score distributions. Our proposed Comprehensive Equity Index (CEI) trade-offs both approaches combining both errors from distribution tails and general distribution shapes. This new metric is well suited to real-world scenarios, as measured on NIST FRVT evaluations, involving high-performance systems and realistic face databases including a wide range of covariates and demographic groups. We first show the limitations of existing metrics to correctly assess the presence of biases in realistic setups and then propose our new metric to tackle these limitations. We tested the proposed metric with two state-of-the-art models and four widely used databases, showing its capacity to overcome the main flaws of previous bias metrics.