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On Mitigating Hard Clusters for Face Clustering

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

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, i.elet@tokeneonedot, 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.

Y. Chen and H. Zhong—Equal contribution.

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Acknowledgments

This work is supported by the National Key R &D Program of China under Grant 2020AAA0103901, Alibaba Group through Alibaba Research Intern Program, and Alibaba Innovative Research (AIR) programme.

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Correspondence to Chong Chen .

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Chen, Y. et al. (2022). On Mitigating Hard Clusters for Face Clustering. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13672. Springer, Cham. https://doi.org/10.1007/978-3-031-19775-8_31

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  • DOI: https://doi.org/10.1007/978-3-031-19775-8_31

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