  Face recognition models trained under the assumption of identical training
and test distributions often suffer from poor generalization when faced with
unknown variations, such as a novel ethnicity or unpredictable individual
make-ups during test time. In this paper, we introduce a novel cross-domain
metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve
face recognition in unseen domains. The CDT loss encourages learning
semantically meaningful features by enforcing compact feature clusters of
identities from one domain, where the compactness is measured by underlying
similarity metrics that belong to another training domain with different
statistics. Intuitively, it discriminatively correlates explicit metrics
derived from one domain, with triplet samples from another domain in a unified
loss function to be minimized within a network, which leads to better alignment
of the training domains. The network parameters are further enforced to learn
generalized features under domain shift, in a model-agnostic learning pipeline.
Unlike the recent work of Meta Face Recognition, our method does not require
careful hard-pair sample mining and filtering strategy during training.
Extensive experiments on various face recognition benchmarks show the
superiority of our method in handling variations, compared to baseline and the
state-of-the-art methods.
