Abstract
Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. We introduce FLUXSynID, a framework for generating high-resolution synthetic face datasets with user-defined identity attribute distributions and paired document-style and trusted live capture images. The dataset generated using the FLUXSynID framework shows improved alignment with real-world identity distributions and greater inter-set diversity compared to prior work. The FLUXSynID framework for generating custom datasets, along with a dataset of 14,889 synthetic identities, is publicly released to support biometric research, including face recognition and morphing attack detection.
Abstract (translated)
合成面部数据集的使用日益增加，旨在克服现实世界生物识别数据的限制，包括隐私问题、人口统计失衡以及高昂的数据收集成本。然而，许多现有的方法缺乏对身份属性的精细控制，并且无法在结构化的拍摄条件下生成成对的一致身份图像。我们引入了FLUXSynID框架，用于根据用户定义的身份属性分布生成高分辨率的合成面部数据集，并生成文档样式和可信实时捕捉的配对图像。 使用FLUXSynID框架生成的数据集中，与真实世界中的身份分布的契合度得到了提升，并且在跨集合多样性方面超越了先前的工作。包括14,889个合成身份的数据集以及用于生成定制数据集的FLUXSynID框架已公开发布，以支持生物识别研究，如面部识别和变体攻击检测。
URL
https://arxiv.org/abs/2505.07530
