  Face recognition has advanced considerably with the availability of
large-scale labeled datasets. However, how to further improve the performance
with the easily accessible unlabeled dataset remains a challenge. In this
paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize
the unlabeled data. We categorize identities in unconstrained environment into
the known set and the unknown set. The former corresponds to the identities
that appear in the labeled training dataset while the latter is its
complementary set. Besides training the model to accurately classify the known
identities, we also force the model to reject unknown identities provided by
the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of
unknown identities, centers of the known identities are forced to keep enough
margin from centers of unknown identities which are assumed to be approximated
by the features of their samples. By this means, the discriminativeness of the
face representations can be enhanced. Experimental results demonstrate that our
approach can provide obvious performance improvement by utilizing the unlabeled
data.
