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.

研究发现，现有的人脸识别系统往往存在对某些民族的偏见，这是因为数据中缺乏对这些民族的充分代表。本研究通过使用合成人脸图像生成方法来改变种族和肤色，以增加数据集的多样性。研究还对使用这样的数据集进行人脸识别系统的适应性进行了详细分析，并使用个体类型角度（ITA）进行真实肤色表示的评估，并使用人脸图像质量评估方法对质量特性进行分析。通过使用四种不同的系统进行全面的人脸识别系统性能分析，本研究为未来的研究工作铺平了道路，包括（i）开发特定民族和一般民族的改变模型，（ii）扩展这样的方法以创建具有多样肤色的数据库，（iii）创建代表各种民族的数据集，进一步在解决隐私问题的同时有助于减轻偏见。