| _version_ | 1866901968726786048 |
|---|---|
| author | Ismayilov, Raul Sero, Dzemila Spreeuwers, Luuk |
| author_facet | Ismayilov, Raul Sero, Dzemila Spreeuwers, Luuk |
| contents | <p dir="ltr">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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17511960 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images Ismayilov, Raul Sero, Dzemila Spreeuwers, Luuk <p dir="ltr">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.</p> |
| title | FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images |
| url | https://doi.org/10.5281/zenodo.17511960 |