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.
Abstract (translated)
面部识别（FR）中的人口统计偏差已成为一个重要的研究领域，因为它对各种应用的公平性、公正性和可靠性产生了影响。随着面部识别技术在全球范围内的广泛应用，这些技术在不同的人口群体——如种族、民族和性别——之间的性能差异引起了广泛关注。这些偏见不仅损害了面部识别系统的可信度，还引发了伦理问题，特别是在这些技术被用于敏感领域时。本文综述了广泛的调研成果，提供了对面部识别中人口统计偏差的多方面概述。我们系统地审视了导致这种人口群体间差异的主要原因、数据集、评估指标和缓解方法。通过在这些领域的关键贡献进行分类，这项工作提供了一种结构化的方法来理解和解决这一问题的复杂性。最后，我们强调了当前的进步，并识别出需要进一步调查的新兴挑战。本文旨在为研究人员提供一个统一的观点，了解该领域最新的研究成果，并强调开发公平且值得信赖的人脸识别系统的重要性。
URL
https://arxiv.org/abs/2502.02309
