Numerous studies have shown that existing Face Recognition Systems (FRS), including commercial ones, often exhibit biases toward certain ethnicities due to under-represented data. In this work, we explore ethnicity alteration and skin tone modification using synthetic face image generation methods to increase the diversity of datasets. We conduct a detailed analysis by first constructing a balanced face image dataset representing three ethnicities: Asian, Black, and Indian. We then make use of existing Generative Adversarial Network-based (GAN) image-to-image translation and manifold learning models to alter the ethnicity from one to another. A systematic analysis is further conducted to assess the suitability of such datasets for FRS by studying the realistic skin-tone representation using Individual Typology Angle (ITA). Further, we also analyze the quality characteristics using existing Face image quality assessment (FIQA) approaches. We then provide a holistic FRS performance analysis using four different systems. Our findings pave the way for future research works in (i) developing both specific ethnicity and general (any to any) ethnicity alteration models, (ii) expanding such approaches to create databases with diverse skin tones, (iii) creating datasets representing various ethnicities which further can help in mitigating bias while addressing privacy concerns.

翻译：大量研究表明，现有的人脸识别系统（包括商业系统）常因训练数据中种族样本不足而表现出对特定族群的偏见。本研究通过合成人脸图像生成方法探索种族特征变换与肤色调整技术，旨在提升数据集的多样性。我们首先构建了包含亚裔、黑人、印度裔三个族群的均衡人脸图像数据集，继而运用基于生成对抗网络的图像到图像翻译模型与流形学习模型实现种族特征转换。通过个体分型角（ITA）评估肤色表征真实性，并结合现有面部图像质量评估（FIQA）方法系统分析数据集质量特性。最终基于四种不同人脸识别系统开展整体性能评估。本研究成果为以下方向奠定基础：（1）开发特定种族与通用（任意种族间）特征变换模型；（2）拓展此类方法构建多样化肤色数据库；（3）创建多族群表征数据集以缓解算法偏见，同时兼顾隐私保护需求。