BoundaryFace: A mining framework with noise label self-correction for Face Recognition

• URL: http://arxiv.org/abs/2210.04567v1
• Date: Mon, 10 Oct 2022 11:12:24 GMT
• Title: BoundaryFace: A mining framework with noise label self-correction for Face Recognition
• Authors: Shijie Wu and Xun Gong
• Abstract summary: 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. The proposed method consistently outperforms SOTA methods in various face recognition benchmarks.
• Score: 9.383955886871743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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://github.com/SWJTU-3DVision/BoundaryFace.

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