The use of large-scale, web-scraped datasets to train
face recognition models has raised significant privacy and
bias concerns. Synthetic methods mitigate these concerns
and provide scalable and controllable face generation to
enable fair and accurate face recognition. However, existing synthetic datasets display limited intraclass and interclass diversity and do not match the face recognition
performance obtained using real datasets. Here, we propose VariFace, a two-stage diffusion-based pipeline to create fair and diverse synthetic face datasets to train face
recognition models. Specifically, we introduce three methods: Face Recognition Consistency to refine demographic
labels, Face Vendi Score Guidance to improve interclass diversity, and Divergence Score Conditioning to balance the
identity preservation-intraclass diversity trade-off. When
constrained to the same dataset size, VariFace considerably
outperforms previous synthetic datasets (0.9200 → 0.9405)
and achieves comparable performance to face recognition
models trained with real data (Real Gap = -0.0065). In
an unconstrained setting, VariFace not only consistently
achieves better performance compared to previous synthetic methods across dataset sizes but also, for the first
time, outperforms the real dataset (CASIA-WebFace) across
six evaluation datasets. This sets a new state-of-the-art
performance with an average face verification accuracy
of 0.9567 (Real Gap = +0.0097) across LFW, CFP-FP,
CPLFW, AgeDB, and CALFW datasets and 0.9366 (Real
Gap = +0.0380) on the RFW dataset.
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