FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images

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.

@article{ismayilov2025_2505.07530,
  title={ FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images },
  author={ Raul Ismayilov and Dzemila Sero and Luuk Spreeuwers },
  journal={arXiv preprint arXiv:2505.07530},
  year={ 2025 }
}