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.

翻译：人脸识别（FR）模型在大型数据集上训练，这些数据集存在隐私和伦理问题。近年来，人们提出使用合成数据来补充或替代真实数据进行人脸识别模型的训练。尽管已取得令人鼓舞的成果，但生成模型能否为此类任务提供足够多样化的数据仍不明确。本文提出了一种新方法，该方法受随机布朗力作用下软颗粒物理运动的启发，能够在多种约束下对潜空间中的身份分布进行采样。基于此，我们生成了多个人脸数据集，并通过训练人脸识别模型对其进行基准测试，结果表明，使用我们的方法生成的数据性能优于以往基于生成对抗网络的数据集，并与基于扩散模型的先进合成数据集性能相当。我们还展示了该方法可用于缓解生成器训练集的数据泄露问题，并探索生成模型生成超出训练集范围数据的能力。