Demographic Fairness in Face Identification: The Watchlist Imbalance Effect

TL;DR

This paper investigates how demographic imbalances in face recognition databases affect system performance, presenting a theoretical estimation method and experimental results highlighting significant impacts of database composition.

Contribution

It introduces a theoretical approach to estimate the watchlist imbalance effect and provides empirical analysis on demographic subsets using the ArcFace system.

Findings

Database composition significantly impacts identification performance.

Performance differentials are less pronounced in verification scenarios.

First detailed analysis of the watchlist imbalance effect.

Abstract

Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect is referred to as "watchlist imbalance effect". In this work, we present a method to theoretically estimate said effect for a biometric identification system given its verification performance across demographic groups and the composition of the used gallery. Further, we report results for identification experiments on differently composed demographic subsets, i.e. females and males, of the public academic MORPH database using the open-source ArcFace face recognition system. It is shown that the database composition has a huge impact on performance differentials in biometric identification systems, even if performance differentials are less pronounced in the verification scenario. This study represents the first detailed analysis of the watchlist imbalance effect which is expected to be of high interest for future research in the field of facial recognition.