  The common implementation of face recognition systems as a cascade of a
detection stage and a recognition or verification stage can cause problems
beyond failures of the detector. When the detector succeeds, it can detect
faces that cannot be recognized, no matter how capable the recognition system.
Recognizability, a latent variable, should therefore be factored into the
design and implementation of face recognition systems. We propose a measure of
recognizability of a face image that leverages a key empirical observation: an
embedding of face images, implemented by a deep neural network trained using
mostly recognizable identities, induces a partition of the hypersphere whereby
unrecognizable identities cluster together. This occurs regardless of the
phenomenon that causes a face to be unrecognizable, it be optical or motion
blur, partial occlusion, spatial quantization, poor illumination. Therefore, we
use the distance from such an "unrecognizable identity" as a measure of
recognizability, and incorporate it in the design of the over-all system. We
show that accounting for recognizability reduces error rate of single-image
face recognition by 58% at FAR=1e-5 on the IJB-C Covariate Verification
benchmark, and reduces verification error rate by 24% at FAR=1e-5 in set-based
recognition on the IJB-C benchmark.

Computer Vision and Pattern Recognition