  Face recognition in unconstrained environments is challenging due to
variations in illumination, quality of sensing, motion blur and etc. An
individual's face appearance can vary drastically under different conditions
creating a gap between train (source) and varying test (target) data. The
domain gap could cause decreased performance levels in direct knowledge
transfer from source to target. Despite fine-tuning with domain specific data
could be an effective solution, collecting and annotating data for all domains
is extremely expensive. To this end, we propose a self-supervised domain
learning (SSDL) scheme that trains on triplets mined from unlabelled data. A
key factor in effective discriminative learning, is selecting informative
triplets. Building on most confident predictions, we follow an "easy-to-hard"
scheme of alternate triplet mining and self-learning. Comprehensive experiments
on four different benchmarks show that SSDL generalizes well on different
domains.
