Face recognition based on adaptive margin and diversity regularization constraints

By Zhemin Zhang, Xun Gong, Junzhou Chen. 2020

10 citations recorded by the source corpus.

Funding information National Natural Science Foundation of China, Grant/Award Number: 61876158; Sichuan Science and Technology Program, Grant/Award Number: 2019YFS0432; National Key Research and Development Project, Grant/Award Number: 2016YFC0802209; Key Research and Development Program of Guangzhou, Grant/Award Number: 202007050002 Abstract In recent years, a more robust facial feature can be learned by convolutional neural networks once introducing margins into loss functions. Those methods set a margin for each class manually to squeeze the intra-class variations within each class equally. However, the internal feature distributions of different persons in the real world are highly unbalanced, and the distance between different identities is not uniform either. As a result, applying the same margin on all classes might not lead to higher inter-class differences. To address this problem, this paper proposes an adaptive margin based on feature distribution to squeeze the feature interior spaces of different classes. Simultaneously, because the inter-class margin can adequately represent the distribution of different classes in the feature space, this paper proposes a novel diversity regularization method. The regularization weights of each class are dynamically set depending on their margins. This method proposed in this paper is intuitively interpretable and can be easily applied to other classification scenarios. Experiments on current existing benchmarks have demonstrated the superiority of our method over state-of-the-art competitors.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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In Computer Vision and Pattern Recognition

Citations in the corpus

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation (2022, methodology): “We implemented some methods in the face recognition community that might be relevant to our work, including (1) Adaptive-margin-based methods (CurricularFace [17], AdaCos [41], ElasticFace [3], AMRLoss [42]), such as adjusting the margin value according to the intra/inter-class angles.”