  Face clustering is a useful tool for applications like automatic face
annotation and retrieval. The main challenge is that it is difficult to cluster
images from the same identity with different face poses, occlusions, and image
quality. Traditional clustering methods usually ignore the relationship between
individual images and their neighbors which may contain useful context
information. In this paper, we repurpose the well-known Transformer and
introduce a Face Transformer for supervised face clustering. In Face
Transformer, we decompose the face clustering into two steps: relation encoding
and linkage predicting. Specifically, given a face image, a \textbf{relation
encoder} module aggregates local context information from its neighbors and a
\textbf{linkage predictor} module judges whether a pair of images belong to the
same cluster or not. In the local linkage graph view, Face Transformer can
generate more robust node and edge representations compared to existing
methods. Experiments on both MS-Celeb-1M and DeepFashion show that our method
achieves state-of-the-art performance, e.g., 91.12\% in pairwise F-score on
MS-Celeb-1M.
