  Recognizing wild faces is extremely hard as they appear with all kinds of
variations. Traditional methods either train with specifically annotated
variation data from target domains, or by introducing unlabeled target
variation data to adapt from the training data. Instead, we propose a universal
representation learning framework that can deal with larger variation unseen in
the given training data without leveraging target domain knowledge. We firstly
synthesize training data alongside some semantically meaningful variations,
such as low resolution, occlusion and head pose. However, directly feeding the
augmented data for training will not converge well as the newly introduced
samples are mostly hard examples. We propose to split the feature embedding
into multiple sub-embeddings, and associate different confidence values for
each sub-embedding to smooth the training procedure. The sub-embeddings are
further decorrelated by regularizing variation classification loss and
variation adversarial loss on different partitions of them. Experiments show
that our method achieves top performance on general face recognition datasets
such as LFW and MegaFace, while significantly better on extreme benchmarks such
as TinyFace and IJB-S.
