  In recent years, deep metric learning and its probabilistic extensions
claimed state-of-the-art results in the face verification task. Despite
improvements in face verification, probabilistic methods received little
attention in the research community and practical applications. In this paper,
we, for the first time, perform an in-depth analysis of known probabilistic
methods in verification and retrieval tasks. We study different design choices
and propose a simple extension, achieving new state-of-the-art results among
probabilistic methods. Finally, we study confidence prediction and show that it
correlates with data quality, but contains little information about prediction
error probability. We thus provide a new confidence evaluation benchmark and
establish a baseline for future confidence prediction research. PyTorch
implementation is publicly released.
