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BoundaryFace: A Mining Framework with Noise Label Self-correction for Face Recognition

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model’s performance. In this paper, starting from the perspective of decision boundary, we propose a novel mining framework that focuses on the relationship between a sample’s ground truth class center and its nearest negative class center. Specifically, a closed-set noise label self-correction module is put forward, making this framework work well on datasets containing a lot of label noise. The proposed method consistently outperforms SOTA methods in various face recognition benchmarks. Training code has been released at https://gitee.com/swjtugx/classmate/tree/master/OurGroup/BoundaryFace.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (61876158), Fundamental Research Funds for the Central Universities (2682021ZTPY030).

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Correspondence to Xun Gong .

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Wu, S., Gong, X. (2022). BoundaryFace: A Mining Framework with Noise Label Self-correction for Face Recognition. 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 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-19778-9_6

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