Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally. This widespread adoption has uncovered significant performance variation across subjects of different racial profiles leading to focused research attention on racial bias within face recognition spanning both current causation and future potential solutions. In support, this study provides an extensive taxonomic review of research on racial bias within face recognition exploring every aspect and stage of the face recognition processing pipeline. Firstly, we discuss the problem definition of racial bias, starting with race definition, grouping strategies, and the societal implications of using race or race-related groupings. Secondly, we divide the common face recognition processing pipeline into four stages: image acquisition, face localisation, face representation, face verification and identification, and review the relevant corresponding literature associated with each stage. The overall aim is to provide comprehensive coverage of the racial bias problem with respect to each and every stage of the face recognition processing pipeline whilst also highlighting the potential pitfalls and limitations of contemporary mitigation strategies that need to be considered within future research endeavours or commercial applications alike.

翻译：人脸识别是计算机视觉领域学术研究最深入、工业开发最成熟的领域之一，其相关应用已在全球范围广泛部署。然而，这种大规模应用揭示了不同种族群体在识别性能上的显著差异，促使学术界关注人脸识别中的种族偏见问题，涵盖其成因及未来潜在解决方案。为支持这一研究，本文对人脸识别中种族偏见的相关研究进行了系统的分类综述，从人脸识别处理流程的各个环节和阶段展开全面探讨。首先，我们讨论种族偏见的定义，包括种族的概念界定、分类策略，以及使用种族或种族相关分类的社会影响。其次，将人脸识别通用处理流程分为四个阶段：图像采集、人脸定位、人脸表征、人脸验证与识别，并综述各阶段的相关文献。总体目标是全面覆盖人脸识别处理流程中每一环节的种族偏见问题，同时指出现有缓解策略的潜在缺陷与局限性，这些因素需在未来的学术研究或商业应用中加以考量。