  Despite the great success of face recognition techniques, recognizing persons
under unconstrained settings remains challenging. Issues like profile views,
unfavorable lighting, and occlusions can cause substantial difficulties.
Previous works have attempted to tackle this problem by exploiting the context,
e.g. clothes and social relations. While showing promising improvement, they
are usually limited in two important aspects, relying on simple heuristics to
combine different cues and separating the construction of context from people
identities. In this work, we aim to move beyond such limitations and propose a
new framework to leverage context for person recognition. In particular, we
propose a Region Attention Network, which is learned to adaptively combine
visual cues with instance-dependent weights. We also develop a unified
formulation, where the social contexts are learned along with the reasoning of
people identities. These models substantially improve the robustness when
working with the complex contextual relations in unconstrained environments. On
two large datasets, PIPA and Cast In Movies (CIM), a new dataset proposed in
this work, our method consistently achieves state-of-the-art performance under
multiple evaluation policies.
