  While deep face recognition has benefited significantly from large-scale
labeled data, current research is focused on leveraging unlabeled data to
further boost performance, reducing the cost of human annotation. Prior work
has mostly been in controlled settings, where the labeled and unlabeled data
sets have no overlapping identities by construction. This is not realistic in
large-scale face recognition, where one must contend with such overlaps, the
frequency of which increases with the volume of data. Ignoring identity overlap
leads to significant labeling noise, as data from the same identity is split
into multiple clusters. To address this, we propose a novel identity separation
method based on extreme value theory. It is formulated as an
out-of-distribution detection algorithm, and greatly reduces the problems
caused by overlapping-identity label noise. Considering cluster assignments as
pseudo-labels, we must also overcome the labeling noise from clustering errors.
We propose a modulation of the cosine loss, where the modulation weights
correspond to an estimate of clustering uncertainty. Extensive experiments on
both controlled and real settings demonstrate our method's consistent
improvements over supervised baselines, e.g., 11.6% improvement on IJB-A
verification.
