Abstract
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.
Abstract (translated)
许多研究都表明，现有的Face Recognition系统（包括商业系统）往往因为代表性数据不足而倾向于针对某些民族产生偏见。在这项工作中，我们使用合成面部图像生成方法来探讨种族改变和肤色修改，以增加数据集的多样性。我们首先构建了一个代表三个民族的平衡面部图像数据集，然后利用现有的基于生成对抗网络（GAN）的图像到图像转换和多态学习模型，将一种民族的肤色改变为另一种民族。我们进一步研究了这种数据集对Face Recognition System（FRS）的适用性，通过研究个体典型角度（ITA）来评估肤色现实主义表示。此外，我们还分析了使用现有的面部图像质量评估（FIQA）方法来评估质量特征。然后，我们使用四种不同的系统提供了全面的FRS性能分析。我们的研究结果为未来研究奠定了基础：（一）开发既针对特定民族又针对任意民族改变模型的可能性；（二）将这种方法扩展到创建具有不同肤色的数据库的可能性；（三）创建代表各种民族的數據集，从而在减轻偏见的同时解决隐私问题。
URL
https://arxiv.org/abs/2405.01273
