  Face clustering is a promising method for annotating unlabeled face images.
Recent supervised approaches have boosted the face clustering accuracy greatly,
however their performance is still far from satisfactory. These methods can be
roughly divided into global-based and local-based ones. Global-based methods
suffer from the limitation of training data scale, while local-based ones are
difficult to grasp the whole graph structure information and usually take a
long time for inference. Previous approaches fail to tackle these two
challenges simultaneously. To address the dilemma of large-scale training and
efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC)
method. Specifically, we design a structure-preserved subgraph sampling
strategy to explore the power of large-scale training data, which can increase
the training data scale from ${10^{5}}$ to ${10^{7}}$. During inference, the
STAR-FC performs efficient full-graph clustering with two steps: graph parsing
and graph refinement. And the concept of node intimacy is introduced in the
second step to mine the local structural information. The STAR-FC gets 91.97
pairwise F-score on partial MS1M within 310s which surpasses the
state-of-the-arts. Furthermore, we are the first to train on very large-scale
graph with 20M nodes, and achieve superior inference results on 12M testing
data. Overall, as a simple and effective method, the proposed STAR-FC provides
a strong baseline for large-scale face clustering. Code is available at
\url{https://sstzal.github.io/STAR-FC/}.
