IDSync: Improving Diffusion Models Through Identity Classification

Effective training of face recognition models requires large-scale datasets of facial identities, yet collecting suitable data is time-consuming and raises privacy concerns. Existing deep generative models offer a promising alternative through the synthesis of high-quality images but often fail to fully preserve identity information. In this work, we propose IDSync, a novel generative diffusion-based framework designed to produce synthetic face images with more consistent identities that are better suited for training recognition models. To this end, IDSync employs a denoising network in the latent space of a frozen variational autoencoder, with identity guidance introduced via a text encoder that interprets identity embeddings from a pretrained recognition model. During training, the framework leverages a pretrained auxiliary identity classifier to define an additional cross-entropy loss, which is backpropagated to improve identity consistency. We evaluate the generated images using inter- and intra-class cosine similarity of identity features along with a variety of statistical measures between synthetic and real distributions focused on fidelity and diversity. To assess utility, we train face recognition models on the synthetic images and measure accuracy on standard verification benchmarks. Experimental results show that recognition models trained on IDSync-generated data achieve higher verification accuracies on real-world benchmarks than models trained on synthetic data produced by competing generative models. The IDSync source code is publicly available at https://github. com/JSabadin/IDSync.