Review of Demographic Bias in Face Recognition

2025-02-04

AI Summary

This paper systematically reviews algorithmic bias in face recognition (FR) arising from demographic attributes such as race and gender. Addressing challenges—including heterogeneous causal factors, inconsistent evaluation criteria, and fragmented mitigation strategies—it introduces, for the first time, a multidimensional classification framework and a unified analytical paradigm. The framework integrates benchmark datasets (e.g., RFW, BUPT-Balanced), fairness metrics (e.g., AUC gap, TPR disparity), and governance strategies to enable structured analysis across the full pipeline: bias causation → evaluation → mitigation. Its key contribution lies in establishing the first authoritative knowledge system that comprehensively covers technical bias mechanisms, quantitative assessment methodologies, and collaborative governance pathways. This system serves as a foundational reference for fair FR research and facilitates industry-wide adoption of standardized bias detection protocols.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Address demographic bias in face recognition
• Examine causes and mitigation of FR disparities
• Promote equitable and trustworthy FR systems

Innovation

• Systematic examination of demographic bias
• Categorization of key contributions
• Highlighting current advancements and challenges