  Face clustering can provide pseudo-labels to the massive unlabeled face data
and improve the performance of different face recognition models. The existing
clustering methods generally aggregate the features within subgraphs that are
often implemented based on a uniform threshold or a learned cutoff position.
This may reduce the recall of subgraphs and hence degrade the clustering
performance. This work proposed an efficient neighborhood-aware subgraph
adjustment method that can significantly reduce the noise and improve the
recall of the subgraphs, and hence can drive the distant nodes to converge
towards the same centers. More specifically, the proposed method consists of
two components, i.e. face embeddings enhancement using the embeddings from
neighbors, and enclosed subgraph construction of node pairs for structural
information extraction. The embeddings are combined to predict the linkage
probabilities for all node pairs to replace the cosine similarities to produce
new subgraphs that can be further used for aggregation of GCNs or other
clustering methods. The proposed method is validated through extensive
experiments against a range of clustering solutions using three benchmark
datasets and numerical results confirm that it outperforms the SOTA solutions
in terms of generalization capability.
