ARTICLE Kotwal_TBIOM_2025/IDIAP Review of Demographic Fairness in Face Recognition Kotwal, Ketan Marcel, Sébastien bias Biometrics demographic fairness Differential Performance Face Recognition Trustworthy AI EXTERNAL https://publications.idiap.ch/attachments/papers/2025/Kotwal_TBIOM_2025.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Kotwal_Idiap-RR-01-2025 Related documents IEEE Transactions on Biometrics, Behavior, and Identity Science 2025 The issue of difference in face recognition (FR) performance across demographic groups 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 differences or 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 performance differences in FR across demographic groups. 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. REPORT Kotwal_Idiap-RR-01-2025/IDIAP Review of Demographic Bias in Face Recognition Kotwal, Ketan Marcel, Sébastien Demographic bias Face Recognition EXTERNAL https://publications.idiap.ch/attachments/reports/2025/Kotwal_Idiap-RR-01-2025.pdf PUBLIC Idiap-RR-01-2025 2025 Idiap February 2025 Demographic bias 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 bias 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.