Attribute-Specified Generation And Style-Transfer Diffusion For Face Recognition Enhancement

We propose a face synthesis approach to augment the training data of a face recognition model for performance improvement. Our approach consists of two major modules, the Source Face Generator (SFG) and the Style-Transfer Diffusion (STD) module. The SFG, composed of a prompt maker and a Multi-Modal Diffusion Transformer (DiT) model, aims to generate diversified and unique frontal faces, each tailored to a specific ethnicity, gender, age, and country group. The STD is designed to duplicate styles from a given set of reference faces onto the source faces generated by SFG while meticulously preserving the source facial identity. Our approach not only preserves the source facial identity within specific demographic groups but also enhances the diversity of the generated faces, effectively mitigating the attribute imbalance issue associated with real-world data. Experiments show that the training data augmented by the synthetic data made by our approach leads to clear performance improvement on state-of-the-art face recognition models.