<p>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.</p>