  Unconstrained video-based face recognition is a challenging problem due to
significant within-video variations caused by pose, occlusion and blur. To
tackle this problem, an effective idea is to propagate the identity from
high-quality faces to low-quality ones through contextual connections, which
are constructed based on context such as body appearance. However, previous
methods have often propagated erroneous information due to lack of uncertainty
modeling of the noisy contextual connections. In this paper, we propose the
Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation
between tracklets, which are represented by nodes in a graph. UGG explicitly
models the uncertainty of the contextual connections by adaptively updating the
weights of the edge gates according to the identity distributions of the nodes
during inference. UGG is a generic graphical model that can be applied at only
inference time or with end-to-end training. We demonstrate the effectiveness of
UGG with state-of-the-art results in the recently released challenging Cast
Search in Movies and IARPA Janus Surveillance Video Benchmark dataset.
