Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition

TL;DR

This paper emphasizes the importance of accurately assessing uncertainty in ROC curves for face recognition fairness, providing theoretical guarantees and practical methods to improve reliability in societal applications.

Contribution

It offers asymptotic guarantees for empirical ROC curves and introduces a recentering bootstrap technique to improve fairness assessment in face recognition.

Findings

Theoretical guarantees for ROC curve uncertainty estimation.

Identification of bootstrap pitfalls and a recentering solution.

Empirical validation on real face datasets confirms method effectiveness.

Abstract

The ROC curve is the major tool for assessing not only the performance but also the fairness properties of a similarity scoring function. In order to draw reliable conclusions based on empirical ROC analysis, accurately evaluating the uncertainty level related to statistical versions of the ROC curves of interest is absolutely necessary, especially for applications with considerable societal impact such as Face Recognition. In this article, we prove asymptotic guarantees for empirical ROC curves of similarity functions as well as for by-product metrics useful to assess fairness. We also explain that, because the false acceptance/rejection rates are of the form of U-statistics in the case of similarity scoring, the naive bootstrap approach may jeopardize the assessment procedure. A dedicated recentering technique must be used instead. Beyond the theoretical analysis carried out, various experiments using real face image datasets provide strong empirical evidence of the practical relevance of the methods promoted here, when applied to several ROC-based measures such as popular fairness metrics.