生成可控身份的合成人脸数据，支持证件与真实场景图像
FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
- 基于用户定义的身份属性分布生成合成人脸
- 产出14,889个身份的高分辨率数据集，身份一致性强
- 适合人脸识别与骗术检测研究者使用
合成人脸数据集被越来越多地用于克服真实生物特征数据的局限性，包括隐私问题、人口统计偏差和高昂的采集成本。然而，许多现有方法在身份属性的细粒度控制上不足，且无法在结构化采集条件下生成配对的身份一致图像。我们提出FLUXSynID框架，可生成高分辨率合成人脸数据集，包含14,889个合成身份。该框架支持自定义身份属性分布，生成兼具证件照风格与可信真实拍摄效果的图像。相较于以往工作，所生成数据在身份分布真实性与类间多样性方面均有提升。本研究公开发布，旨在支持人脸识别与伪造攻击检测等生物特征研究。
原文摘要 · Abstract (English)
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 along with a dataset of 14,889 synthetic identities. We generate synthetic faces with user-defined identity attribute distributions, offering both 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-class diversity compared to prior work. Our work is publicly released to support biometric research, including face recognition and morphing attack detection.
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