Face Clustering via Early Stopping and Edge Recall

• URL: http://arxiv.org/abs/2408.13431v1
• Date: Sat, 24 Aug 2024 01:53:02 GMT
• Title: Face Clustering via Early Stopping and Edge Recall
• Authors: Junjie Liu,
• Abstract summary: We propose a novel unsupervised face clustering algorithm FC-ES and a novel supervised face clustering algorithm FC-ESER. Our proposed FC-ES and FC-ESER significantly outperform previous state-of-the-art methods.
• Score: 6.847295047818357
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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