  Open-set face recognition refers to a scenario in which biometric systems
have incomplete knowledge of all existing subjects. Therefore, they are
expected to prevent face samples of unregistered subjects from being identified
as previously enrolled identities. This watchlist context adds an arduous
requirement that calls for the dismissal of irrelevant faces by focusing mainly
on subjects of interest. As a response, this work introduces a novel method
that associates an ensemble of compact neural networks with a margin-based cost
function that explores additional samples. Supplementary negative samples can
be obtained from external databases or synthetically built at the
representation level in training time with a new mix-up feature augmentation
approach. Deep neural networks pre-trained on large face datasets serve as the
preliminary feature extraction module. We carry out experiments on well-known
LFW and IJB-C datasets where results show that the approach is able to boost
closed and open-set identification rates.
