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SubFace: learning with softmax approximation for face recognition

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Abstract

The softmax-based loss function and its variants (e.g., cosface, sphereface, and arcface) significantly improve the face recognition performance in wild unconstrained scenes. A common practice of these algorithms is to perform optimizations on the multiplication between the embedding features and the linear transformation matrix. However in most cases, the dimension of embedding features is given based on traditional design experience, and there is less-studied on improving performance using the feature itself when giving a fixed size. To address this challenge, this paper presents a softmax approximation method called SubFace, which employs the subspace feature to promote the performance of face recognition. Specifically, we dynamically select the non-overlapping subspace features in each batch during training, and then use the subspace features to approximate full-feature among softmax-based loss, so the discriminability of the deep model can be significantly enhanced for face recognition. Comprehensive experiments conducted on benchmark datasets demonstrate that our method can significantly improve the performance of vanilla CNN baseline, which strongly proves the effectiveness of the subspace strategy with the margin-based loss, e.g. ArcFace with our strategy can achieve the best performance of 99.85% and 93.48% on LFW and CPLFW dataset respectively.

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Availability of data and material

The data used for the experiments in this paper are available online, see Section 4.1 for more details.

Code Availibility

The code will be publicly available once the work is published upon agreement of different sides.

Notes

  1. The feature sampling rate of 1.0 represents the original training strategy.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant No.62301315, Startup Fund for Young Faculty at SJTU under Grant No.23X010501967 and Shanghai Municipal Health Commission Health Industry Clinical Research Special Project under Grant No.202340010. The authors would like to thank the anonymous reviewers for their valuable suggestions and constructive criticisms.

Funding

This work was partially supported by the National Natural Science Foundation of China under Grant No. 62301315, Startup Fund for Young Faculty at SJTU under Grant No. 23X010501967 and Shanghai Municipal Health Commission Health Industry Clinical Research Special Project under Grant No. 202340010.

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Authors and Affiliations

  1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Suncheng Xiang, Hongwei Xu & Dahong Qian

  2. Student Innovation Center, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Mingye Xie

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  1. Suncheng Xiang
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  2. Hongwei Xu
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Contributions

Suncheng Xiang and Hongwei Xu contributed conception and design of the study, as well as the experimental process and interpreted model results. Suncheng Xiang obtained funding for the project and provided clinical guidance. Hongwei Xu, Mingye Xie and Suncheng Xiang drafted the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.

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Correspondence to Suncheng Xiang.

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Xiang, S., Xu, H., Xie, M. et al. SubFace: learning with softmax approximation for face recognition. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19037-7

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