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）常因数据表征不足而对某些种族存在偏见。本研究通过合成人脸图像生成方法探索种族特征改变与肤色调整技术，以提升数据集多样性。我们首先构建了包含亚裔、非裔和印度裔三种族的均衡人脸图像数据集，随后利用现有基于生成对抗网络的图像到图像翻译与流形学习模型实现种族特征的跨类别转换。通过个体类型学角度研究真实肤色表征，系统评估此类数据集对人脸识别系统的适用性；同时采用现有面部图像质量评估方法分析图像质量特征。最终基于四种不同系统开展全面的人脸识别系统性能分析。本研究为以下方向奠定基础：（1）开发特定种族与通用种族转换模型，（2）扩展此类方法以构建多样肤色数据库，（3）创建多族裔表征数据集，在解决隐私问题的同时缓解算法偏见。