Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation

TL;DR

This paper introduces a hybrid generative approach combining diffusion and GAN models to efficiently create a diverse, privacy-preserving face dataset that enhances recognition performance, winning first place in a challenge.

Contribution

It presents a novel hybrid dataset generation method using diffusion and GAN models, with a MoE-based cleaning process and curriculum learning for privacy and diversity.

Findings

Achieved first place in the DataCV ICCV Challenge.

Improved face recognition accuracy across multiple dataset scales.

Generated high-quality, diverse synthetic identities without identity leakage.

Abstract

In this paper, we present our approach to the DataCV ICCV Challenge, which centers on building a high-quality face dataset to train a face recognition model. The constructed dataset must not contain identities overlapping with any existing public face datasets. To handle this challenge, we begin with a thorough cleaning of the baseline HSFace dataset, identifying and removing mislabeled or inconsistent identities through a Mixture-of-Experts (MoE) strategy combining face embedding clustering and GPT-4o-assisted verification. We retain the largest consistent identity cluster and apply data augmentation up to a fixed number of images per identity. To further diversify the dataset, we generate synthetic identities using Stable Diffusion with prompt engineering. As diffusion models are computationally intensive, we generate only one reference image per identity and efficiently expand it using Vec2Face, which rapidly produces 49 identity-consistent variants. This hybrid approach fuses GAN-based and diffusion-based samples, enabling efficient construction of a diverse and high-quality dataset. To address the high visual similarity among synthetic identities, we adopt a curriculum learning strategy by placing them early in the training schedule, allowing the model to progress from easier to harder samples. Our final dataset contains 50 images per identity, and all newly generated identities are checked with mainstream face datasets to ensure no identity leakage. Our method achieves \textbf{1st place} in the competition, and experimental results show that our dataset improves model performance across 10K, 20K, and 100K identity scales. Code is available at https://github.com/Ferry-Li/datacv_fr.