Given a large number of unlabeled face images, face grouping aims at
clustering the images into individual identities present in the data. This task
remains a challenging problem despite the remarkable capability of deep
learning approaches in learning face representation. In particular, grouping
results can still be egregious given profile faces and a large number of
uninteresting faces and noisy detections. Often, a user needs to correct the
erroneous grouping manually. In this study, we formulate a novel face grouping
framework that learns clustering strategy from ground-truth simulated behavior.
This is achieved through imitation learning (a.k.a apprenticeship learning or
learning by watching) via inverse reinforcement learning (IRL). In contrast to
existing clustering approaches that group instances by similarity, our
framework makes sequential decision to dynamically decide when to merge two
face instances/groups driven by short- and long-term rewards. Extensive
experiments on three benchmark datasets show that our framework outperforms
unsupervised and supervised baselines
