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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References
Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retr. 12(4), 461–486 (2009)
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Banerjee, A., Krumpelman, C., Ghosh, J., Basu, S., Mooney, R.J.: Model-based overlapping clustering. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 532–537 (2005)
Breiman, L., Meisel, W., Purcell, E.: Variable kernel estimates of multivariate densities. Technometrics 19(2), 135–144 (1977)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR (2019)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: SIGKDD (1996)
Guo, S., Xu, J., Chen, D., Zhang, C., Wang, X., Zhao, R.: Density-aware feature embedding for face clustering. In: CVPR (2020)
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6
Ivchenko, G., Honov, S.: On the Jaccard similarity test. J. Math. Sci. 88(6), 789–794 (1998)
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The MegaFace Benchmark: 1 million faces for recognition at scale. In: CVPR (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kortli, Y., Jridi, M., Al Falou, A., Atri, M.: Face recognition systems: a survey. Sensors 20(2), 342 (2020)
Liu, J., Qiu, D., Yan, P., Wei, X.: Learn to cluster faces via pairwise classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3845–3853 (2021)
Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: CVPR (2017)
Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin Softmax loss for convolutional neural networks. In: ICML (2016)
Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096–1104 (2016)
Lloyd, S.: Least squares quantization in PCM. TIP 28, 129–137 (1982)
Nguyen, X.B., Bui, D.T., Duong, C.N., Bui, T.D., Luu, K.: Clusformer: a transformer based clustering approach to unsupervised large-scale face and visual landmark recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10847–10856 (2021)
Otto, C., Wang, D., Jain, A.K.: Clustering millions of faces by identity. TPAMI 40, 289–303 (2017)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition (2015)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Shen, S., et al.: Structure-aware face clustering on a large-scale graph with 107 nodes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9085–9094 (2021)
Sibson, R.: Slink: an optimally efficient algorithm for the single-link cluster method. Comput. J. 16, 30–34 (1973)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Vaswani, A., et al.: Attention is all you need. In: NIPS (2017)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: CVPR (2018)
Wang, Y., et al.: Ada-NETS: face clustering via adaptive neighbour discovery in the structure space. arXiv preprint arXiv:2202.03800 (2022)
Wang, Z., Zheng, L., Li, Y., Wang, S.: Linkage based face clustering via graph convolution network. In: CVPR (2019)
Xiong, R., et al.: On layer normalization in the transformer architecture. In: ICML, pp. 10524–10533. PMLR (2020)
Yang, L., Chen, D., Zhan, X., Zhao, R., Loy, C.C., Lin, D.: Learning to cluster faces via confidence and connectivity estimation. In: CVPR (2020)
Yang, L., Zhan, X., Chen, D., Yan, J., Loy, C.C., Lin, D.: Learning to cluster faces on an affinity graph. In: CVPR (2019)
Zhan, X., Liu, Z., Yan, J., Lin, D., Loy, C.C.: Consensus-driven propagation in massive unlabeled data for face recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 576–592. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_35
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
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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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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