  The availability of large-scale authentic face databases has been crucial to
the significant advances made in face recognition research over the past
decade. However, legal and ethical concerns led to the recent retraction of
many of these databases by their creators, raising questions about the
continuity of future face recognition research without one of its key
resources. Synthetic datasets have emerged as a promising alternative to
privacy-sensitive authentic data for face recognition development. However,
recent synthetic datasets that are used to train face recognition models suffer
either from limitations in intra-class diversity or cross-class (identity)
discrimination, leading to less optimal accuracies, far away from the
accuracies achieved by models trained on authentic data. This paper targets
this issue by proposing IDiff-Face, a novel approach based on conditional
latent diffusion models for synthetic identity generation with realistic
identity variations for face recognition training. Through extensive
evaluations, our proposed synthetic-based face recognition approach pushed the
limits of state-of-the-art performances, achieving, for example, 98.00%
accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the
recent synthetic-based face recognition solutions with 95.40% and bridging the
gap to authentic-based face recognition with 99.82% accuracy.
