Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion

arXiv:2405.00228v1 Announce Type: new
Abstract: 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 …

abstract arxiv concerns cs.cv data datasets diffusion ethical exploration face face recognition identity privacy recognition results scale space synthetic synthetic data training type via while