  Face clustering is a promising way to scale up face recognition systems using
large-scale unlabeled face images. It remains challenging to identify small or
sparse face image clusters that we call hard clusters, which is caused by the
heterogeneity, \ie, high variations in size and sparsity, of the clusters.
Consequently, the conventional way of using a uniform threshold (to identify
clusters) often leads to a terrible misclassification for the samples that
should belong to hard clusters. We tackle this problem by leveraging the
neighborhood information of samples and inferring the cluster memberships (of
samples) in a probabilistic way. We introduce two novel modules,
Neighborhood-Diffusion-based Density (NDDe) and Transition-Probability-based
Distance (TPDi), based on which we can simply apply the standard Density Peak
Clustering algorithm with a uniform threshold. Our experiments on multiple
benchmarks show that each module contributes to the final performance of our
method, and by incorporating them into other advanced face clustering methods,
these two modules can boost the performance of these methods to a new
state-of-the-art. Code is available at:
https://github.com/echoanran/On-Mitigating-Hard-Clusters.
