Invface: inversion-based synthetic face recognition

This is the authors' abstract. We don't add key points for this paper.

Facial recognition technology has achieved remarkable accuracy across various applications, but its reliance on large-scale real face datasets raises significant privacy and ethical concerns. To address these challenges, we propose Invface, a novel synthetic face recognition (SFR) method leveraging diffusion denoising implicit models (DDIMs) to generate high-quality synthetic face datasets. InvFace ensures identity consistency and intra-class diversity by disentangling style and background semantics from real images through a conditional reverse sampling strategy. Our method effectively synthesizes diverse facial images while preserving identity fidelity, outperforming state-of-the-art synthetic approaches on benchmark datasets. Experiments demonstrate that face recognition models trained on Invface datasets achieve competitive accuracy comparable to those trained on real data, offering a robust solution to privacy issues in real-world face recognition. Additionally, privacy analysis confirms that InvFace generates novel virtual identities distinct from training data.