  In this paper, we present an accurate and scalable approach to the face
clustering task. We aim at grouping a set of faces by their potential
identities. We formulate this task as a link prediction problem: a link exists
between two faces if they are of the same identity. The key idea is that we
find the local context in the feature space around an instance (face) contains
rich information about the linkage relationship between this instance and its
neighbors. By constructing sub-graphs around each instance as input data, which
depict the local context, we utilize the graph convolution network (GCN) to
perform reasoning and infer the likelihood of linkage between pairs in the
sub-graphs. Experiments show that our method is more robust to the complex
distribution of faces than conventional methods, yielding favorably comparable
results to state-of-the-art methods on standard face clustering benchmarks, and
is scalable to large datasets. Furthermore, we show that the proposed method
does not need the number of clusters as prior, is aware of noises and outliers,
and can be extended to a multi-view version for more accurate clustering
accuracy.
