  Understanding videos such as TV series and movies requires analyzing who the
characters are and what they are doing. We address the challenging problem of
clustering face tracks based on their identity. Different from previous work in
this area, we choose to operate in a realistic and difficult setting where: (i)
the number of characters is not known a priori; and (ii) face tracks belonging
to minor or background characters are not discarded.
  To this end, we propose Ball Cluster Learning (BCL), a supervised approach to
carve the embedding space into balls of equal size, one for each cluster. The
learned ball radius is easily translated to a stopping criterion for iterative
merging algorithms. This gives BCL the ability to estimate the number of
clusters as well as their assignment, achieving promising results on commonly
used datasets. We also present a thorough discussion of how existing metric
learning literature can be adapted for this task.
