  Face clustering is an essential tool for exploiting the unlabeled face data,
and has a wide range of applications including face annotation and retrieval.
Recent works show that supervised clustering can result in noticeable
performance gain. However, they usually involve heuristic steps and require
numerous overlapped subgraphs, severely restricting their accuracy and
efficiency. In this paper, we propose a fully learnable clustering framework
without requiring a large number of overlapped subgraphs. Instead, we transform
the clustering problem into two sub-problems. Specifically, two graph
convolutional networks, named GCN-V and GCN-E, are designed to estimate the
confidence of vertices and the connectivity of edges, respectively. With the
vertex confidence and edge connectivity, we can naturally organize more
relevant vertices on the affinity graph and group them into clusters.
Experiments on two large-scale benchmarks show that our method significantly
improves clustering accuracy and thus performance of the recognition models
trained on top, yet it is an order of magnitude more efficient than existing
supervised methods.
