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 this https URL

本文提出了一种通过元学习来实现对新的未见领域进行面部识别的方法，该方法名为 “Meta Face Recognition（MFR）”，该方法利用元优化目标综合源 / 目标域转移，并通过优化多域分布来合并梯度 / 元梯度以提高泛化性能，同时提出了两个用于泛化人脸识别评估的基准，实验结果证实了该方法的优越性能。