  Face recognition systems are usually faced with unseen domains in real-world
applications and show unsatisfactory performance due to their poor
generalization. For example, a well-trained model on webface data cannot deal
with the ID vs. Spot task in surveillance scenario. In this paper, we aim to
learn a generalized model that can directly handle new unseen domains without
any model updating. To this end, we propose a novel face recognition method via
meta-learning named Meta Face Recognition (MFR). MFR synthesizes the
source/target domain shift with a meta-optimization objective, which requires
the model to learn effective representations not only on synthesized source
domains but also on synthesized target domains. Specifically, we build
domain-shift batches through a domain-level sampling strategy and get
back-propagated gradients/meta-gradients on synthesized source/target domains
by optimizing multi-domain distributions. The gradients and meta-gradients are
further combined to update the model to improve generalization. Besides, we
propose two benchmarks for generalized face recognition evaluation. Experiments
on our benchmarks validate the generalization of our method compared to several
baselines and other state-of-the-arts. The proposed benchmarks will be
available at https://github.com/cleardusk/MFR.
