通过合成族裔改变实现包容性人脸识别
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration
AI总结：
本文构建涵盖亚洲人、黑人和印度人的平衡人脸数据集，利用GAN图像转换与流形学习进行合成族裔改变，并通过ITA、FIQA及四种FRS评估其在缓解偏见中的作用。
摘要：
大量研究表明，现有的人脸识别系统（FRS），包括商用系统，往往因代表性不足的数据而对某些族裔表现出偏见。在本研究中，我们探索利用合成人脸图像生成方法进行族裔改变和肤色修改，以提升数据集的多样性。我们开展了详细分析：首先构建一个平衡的人脸图像数据集，涵盖亚洲人、黑人和印度人三个族裔。随后，我们利用现有的基于生成对抗网络（GAN）的图像到图像转换和流形学习模型，将族裔从一种改变为另一种。我们进一步通过使用个体类型学角度（ITA）研究真实的肤色表征，系统分析此类数据集对FRS的适用性。此外，我们还使用现有的人脸图像质量评估（FIQA）方法分析质量特征。之后，我们使用四种不同系统提供全面的FRS性能分析。我们的发现为未来研究铺平道路：（i）开发特定族裔和通用（任意到任意）族裔改变模型；（ii）扩展此类方法以创建具有多样肤色的数据库；（iii）创建代表各种族裔的数据集，从而在解决隐私问题的同时帮助缓解偏见。
英文摘要：
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.
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