  Face Recognition (FR) models are trained on large-scale datasets, which have
privacy and ethical concerns. Lately, the use of synthetic data to complement
or replace genuine data for the training of FR models has been proposed. While
promising results have been obtained, it still remains unclear if generative
models can yield diverse enough data for such tasks. In this work, we introduce
a new method, inspired by the physical motion of soft particles subjected to
stochastic Brownian forces, allowing us to sample identities distributions in a
latent space under various constraints. With this in hands, we generate several
face datasets and benchmark them by training FR models, showing that data
generated with our method exceeds the performance of previously GAN-based
datasets and achieves competitive performance with state-of-the-art
diffusion-based synthetic datasets. We also show that this method can be used
to mitigate leakage from the generator's training set and explore the ability
of generative models to generate data beyond it.
