  Face clustering plays an essential role in exploiting massive unlabeled face
data. Recently, graph-based face clustering methods are getting popular for
their satisfying performances. However, they usually suffer from excessive
memory consumption especially on large-scale graphs, and rely on empirical
thresholds to determine the connectivities between samples in inference, which
restricts their applications in various real-world scenes. To address such
problems, in this paper, we explore face clustering from the pairwise angle.
Specifically, we formulate the face clustering task as a pairwise relationship
classification task, avoiding the memory-consuming learning on large-scale
graphs. The classifier can directly determine the relationship between samples
and is enhanced by taking advantage of the contextual information. Moreover, to
further facilitate the efficiency of our method, we propose a rank-weighted
density to guide the selection of pairs sent to the classifier. Experimental
results demonstrate that our method achieves state-of-the-art performances on
several public clustering benchmarks at the fastest speed and shows a great
advantage in comparison with graph-based clustering methods on memory
consumption.
