  With the recent success of deep neural networks, remarkable progress has been
achieved on face recognition. However, collecting large-scale real-world
training data for face recognition has turned out to be challenging, especially
due to the label noise and privacy issues. Meanwhile, existing face recognition
datasets are usually collected from web images, lacking detailed annotations on
attributes (e.g., pose and expression), so the influences of different
attributes on face recognition have been poorly investigated. In this paper, we
address the above-mentioned issues in face recognition using synthetic face
images, i.e., SynFace. Specifically, we first explore the performance gap
between recent state-of-the-art face recognition models trained with synthetic
and real face images. We then analyze the underlying causes behind the
performance gap, e.g., the poor intra-class variations and the domain gap
between synthetic and real face images. Inspired by this, we devise the SynFace
with identity mixup (IM) and domain mixup (DM) to mitigate the above
performance gap, demonstrating the great potentials of synthetic data for face
recognition. Furthermore, with the controllable face synthesis model, we can
easily manage different factors of synthetic face generation, including pose,
expression, illumination, the number of identities, and samples per identity.
Therefore, we also perform a systematically empirical analysis on synthetic
face images to provide some insights on how to effectively utilize synthetic
data for face recognition.
