Structure-enhanced pairwise feature learning for face clustering

Abstract

Face clustering groups massive unlabeled face images according to their underlying identities and has proven to be a valuable tool for data analysis. Most recent studies have utilized graph convolutional networks (GCNs) to explore the structural properties of faces, thereby effectively achieving improved clustering performance. However, these methods usually suffer from computational intractability for large-scale graphs and tend to be sensitive to some postprocessing thresholds that serve to purify the clustering results. To address these issues, in this paper, we consider each pairwise relationship between two samples as a learning unit and infer clustering assignments by evaluating a group of pairwise connections. Specifically, we propose a novel clustering framework, named structure-enhanced pairwise feature learning (SEPFL), which mixes neighborhood information to adaptively produce pairwise representations for cluster identification. In addition, we design a combined density strategy to select representative pairs, thus ensuring training effectiveness and inference efficiency. The extensive experimental results show that SEPFL achieves better performance than other advanced face clustering techniques.