  Biometric recognition has primarily addressed closed-set identification,
assuming all probe subjects are in the gallery. However, most practical
applications involve open-set biometrics, where probe subjects may or may not
be present in the gallery. This poses distinct challenges in effectively
distinguishing individuals in the gallery while minimizing false detections.
While it is commonly believed that powerful biometric models can excel in both
closed- and open-set scenarios, existing loss functions are inconsistent with
open-set evaluation. They treat genuine (mated) and imposter (non-mated)
similarity scores symmetrically and neglect the relative magnitudes of imposter
scores. To address these issues, we simulate open-set evaluation using
minibatches during training and introduce novel loss functions: (1) the
identification-detection loss optimized for open-set performance under
selective thresholds and (2) relative threshold minimization to reduce the
maximum negative score for each probe. Across diverse biometric tasks,
including face recognition, gait recognition, and person re-identification, our
experiments demonstrate the effectiveness of the proposed loss functions,
significantly enhancing open-set performance while positively impacting
closed-set performance. Our code and models are available at
https://github.com/prevso1088/open-set-biometrics.
