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Deep Face Recognition with Cosine Boundary Softmax Loss

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14429))

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Abstract

To improve the accuracy of face recognition when there are wrong-labeled samples, a new deep face recognition model with cosine boundary loss is proposed in this paper. First, the proposed model uses the cosine similarity to determine the boundary that divides training samples into easy samples, semi-hard samples and harder samples, which play different roles during the training process. Then, an adaptive weighted piecewise loss function is developed to emphasize semi-hard samples and suppress wrong-labeled samples in harder samples by assigning different weights to related types of samples during different training stages. Compared with the state-of-the-art face recognition methods, i.e., CosFace, CurricularFace, and EnhanceFace, experimental results on CFP_FF, CFP_FP, AgeDB, LFW, CALFW, CPLFW, VGG2_FP datasets demonstrate that the proposed method can effectively reduce the impact of the wrong-labeled samples and provide a better accuracy.

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Acknowledgements

This study was supported by National Natural Science Foundation of China under Grant 41771375, Grant 31860182, and Grant 41961053, Natural Science Foundation of Henan under Grant 232300421071, Scientific and Technological Innovation Talent in Universities of Henan Province under Grant 22HASTIT015, and Youth key Teacher of Henan under Grant 2020GGJS030.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Henan University, Kaifeng, 475004, China

    Chen Zheng, Yuncheng Chen, Jingying Li & Yongxia Wang

  2. Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Institute of Applied Mathematics, Henan University, Kaifeng, 475004, China

    Chen Zheng, Yuncheng Chen, Jingying Li & Yongxia Wang

  3. Institutes of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, 650224, China

    Leiguang Wang

Authors
  1. Chen Zheng
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  2. Yuncheng Chen
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  3. Jingying Li
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  4. Yongxia Wang
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  5. Leiguang Wang
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Corresponding author

Correspondence to Jingying Li .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zheng, C., Chen, Y., Li, J., Wang, Y., Wang, L. (2024). Deep Face Recognition with Cosine Boundary Softmax Loss. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_24

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  • DOI: https://doi.org/10.1007/978-981-99-8469-5_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8468-8

  • Online ISBN: 978-981-99-8469-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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