Review of Demographic Fairness in Face Recognition

TL;DR

This review paper comprehensively examines demographic fairness issues in face recognition, analyzing causes, datasets, metrics, and mitigation strategies to promote equitable and trustworthy systems.

Contribution

It offers a structured overview of research on demographic disparities in face recognition, highlighting current advancements and future challenges.

Findings

Identification of key causes of demographic bias

Analysis of datasets and assessment metrics used

Overview of mitigation approaches and their effectiveness

Abstract

Demographic fairness in face recognition (FR) has emerged as a critical area of research, given its impact on fairness, equity, and reliability across diverse applications. As FR technologies are increasingly deployed globally, disparities in performance across demographic groups -- such as race, ethnicity, and gender -- have garnered significant attention. These biases not only compromise the credibility of FR systems but also raise ethical concerns, especially when these technologies are employed in sensitive domains. This review consolidates extensive research efforts providing a comprehensive overview of the multifaceted aspects of demographic fairness in FR. We systematically examine the primary causes, datasets, assessment metrics, and mitigation approaches associated with demographic disparities in FR. By categorizing key contributions in these areas, this work provides a structured approach to understanding and addressing the complexity of this issue. Finally, we highlight current advancements and identify emerging challenges that need further investigation. This article aims to provide researchers with a unified perspective on the state-of-the-art while emphasizing the critical need for equitable and trustworthy FR systems.