BIF-Face

Training face recognition models requires a large amount of identity-labeled face images, which are often collected by crawling the web, and therefore have ethical and privacy concerns. Recently, generating synthetic face datasets and training face recognition models using synthetic datasets has emerged to be a viable solution. We present BIF-Face, a new synthetic face recognition dataset. We use the Brownian identity diffusion to generate synthetic identities, and then build synthetic face recognition datasets by generating different samples per each identity using a foundation model. Our experimental results show that face recognition models trained with BIF-Face achieve competitive performance with face recognition models trained on state-of-the-art synthetic face recognition datasets.