梳理人脸识别中的群体公平性问题，揭示偏见根源与解决路径。
Review of Demographic Fairness in Face Recognition
- 系统分析种族、性别等群体在人脸识别中的性能差异成因
- 归纳主流数据集与评估指标，构建公平性研究框架
- 适合关注AI伦理与可信系统的研究人员参考
人脸识别中的群体公平性已成为关键研究领域，因其对公平性、公正性和可靠性的影响。随着人脸识别技术在全球范围的广泛应用，不同群体（如种族、民族、性别）之间的性能差异引发广泛关注。这些偏差不仅损害人脸识别系统的可信度，还在敏感应用场景中引发伦理问题。本文综述了大量研究成果，全面梳理了人脸识别中群体公平性的多维度问题。系统考察了主要成因、常用数据集、评估指标及缓解方法，并对关键贡献进行分类，提供理解与应对该问题的结构化视角。最后，总结当前进展并指出需进一步研究的新兴挑战。本文旨在为研究者提供前沿进展的统一视图，强调构建公平可信人脸识别系统的重要性。
原文摘要 · Abstract (English)
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.
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