Face Clustering via Early Stopping and Edge Recall

TL;DR

This paper introduces two novel face clustering algorithms, FC-ES and FC-ESER, that improve accuracy and recall in large-scale face clustering through early stopping and edge recall strategies, applicable in both unsupervised and supervised settings.

Contribution

The paper proposes new unsupervised and supervised face clustering algorithms with innovative strategies for edge recall and early stopping, enhancing performance and scalability.

Findings

Significantly outperform previous state-of-the-art methods on multiple benchmarks.

Effectively balance accuracy and recall in large-scale face clustering.

Demonstrate robustness across face, person, and vehicle clustering tasks.

Abstract

Large-scale face clustering has achieved significant progress, with many efforts dedicated to learning to cluster large-scale faces with supervised-learning. However, complex model design and tedious clustering processes are typical in existing methods. Such limitations result in infeasible clustering in real-world applications. Reasonable and efficient model design and training need to be taken into account. Besides, developing unsupervised face clustering algorithms is crucial, which are more realistic in real-world applications. In this paper, we propose a novel unsupervised face clustering algorithm FC-ES and a novel supervised face clustering algorithm FC-ESER to address these issues. An efficient and effective neighbor-based edge probability and a novel early stopping strategy are proposed in FC-ES, guaranteeing the accuracy and recall of large-scale face clustering simultaneously. Furthermore, to take advantage of supervised learning, a novel edge recall strategy is proposed in FC-ESER to further recall the edge connections that are not connected in FC-ES. Extensive experiments on multiple benchmarks for face, person, and vehicle clustering show that our proposed FC-ES and FC-ESER significantly outperform previous state-of-the-art methods. Our code will be available at https://github.com/jumptoliujj/FC-ESER.