FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
This addresses the need for privacy-preserving, demographically balanced synthetic data for biometric research, though it is incremental in improving identity control and dataset quality.
The paper tackles the problem of generating synthetic face datasets with fine-grained identity control and structured capture conditions, resulting in a framework that produces 14,889 synthetic identities with improved alignment to real-world distributions and greater inter-class diversity.
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.