  Modeling data uncertainty is important for noisy images, but seldom explored
for face recognition. The pioneer work, PFE, considers uncertainty by modeling
each face image embedding as a Gaussian distribution. It is quite effective.
However, it uses fixed feature (mean of the Gaussian) from an existing model.
It only estimates the variance and relies on an ad-hoc and costly metric. Thus,
it is not easy to use. It is unclear how uncertainty affects feature learning.
  This work applies data uncertainty learning to face recognition, such that
the feature (mean) and uncertainty (variance) are learnt simultaneously, for
the first time. Two learning methods are proposed. They are easy to use and
outperform existing deterministic methods as well as PFE on challenging
unconstrained scenarios. We also provide insightful analysis on how
incorporating uncertainty estimation helps reducing the adverse effects of
noisy samples and affects the feature learning.
