Face clustering is an essential task in computer vision due to the explosion
of related applications such as augmented reality or photo album management.
The main challenge of this task lies in the imperfectness of similarities among
image feature representations. Given an existing feature extraction model, it
is still an unresolved problem that how can the inherent characteristics of
similarities of unlabelled images be leveraged to improve the clustering
performance. Motivated by answering the question, we develop an effective
unsupervised method, named as FaceMap, by formulating face clustering as a
process of non-overlapping community detection, and minimizing the entropy of
information flows on a network of images. The entropy is denoted by the map
equation and its minimum represents the least description of paths among images
in expectation. Inspired by observations on the ranked transition probabilities
in the affinity graph constructed from facial images, we develop an outlier
detection strategy to adaptively adjust transition probabilities among images.
Experiments with ablation studies demonstrate that FaceMap significantly
outperforms existing methods and achieves new state-of-the-arts on three
popular large-scale datasets for face clustering, e.g., an absolute improvement
of more than $10%$ and $4%$ comparing with prior unsupervised and supervised
methods respectively in terms of average of Pairwise F-score. Our code is
publicly available on github.