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Adaptive Prototype Triplet Loss for Cross-Resolution Face Recognition

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 704))

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Abstract

Although face recognition has achieved great success in many areas, cross-resolution face recognition (CRFR) still remains a challenging task due to the large domain gap between low-resolution (LR) and high-resolution (HR) images. In this paper, we propose an adaptive prototype triplet loss (APTL) for CRFR. The APTL pulls the features close to their own prototypes, and pushes them away from the prototypes of other classes. Thus, the angular distances between features and prototypes from the same class are closer than those from different classes. Furthermore, to better exploit the similarity information among different identities, we adaptively adjust the margin term in the loss. Since the proposed APTL is applied simultaneously to HR and LR features, the gap between two domains can be narrowed naturally. Experiments on LFW and SCface datasets illustrate the superiority of our method.

Y. Chen and W. Zheng—Co-first authors.

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Acknowledgements

This work was partly supported by Special Foundations for the Development of Strategic Emerging Industries of Shenzhen

(Nos. KJZD20231023094700001 & CJGJZD20210408092804011).

Author information

Authors and Affiliations

  1. Shenzhen International Graduate Department of Electronic Engineering, Tsinghua University, Beijing, China

    Yongru Chen, Wenxian Zheng, Xiaying Bai, Qiqi Bao, Wenming Yang & Qingmin Liao

  2. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Guijin Wang

Authors
  1. Yongru Chen
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  2. Wenxian Zheng
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  3. Xiaying Bai
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  4. Qiqi Bao
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  5. Wenming Yang
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  6. Guijin Wang
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  7. Qingmin Liao
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Corresponding author

Correspondence to Wenming Yang .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Chen, Y. et al. (2024). Adaptive Prototype Triplet Loss for Cross-Resolution Face Recognition. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704. Springer, Cham. https://doi.org/10.1007/978-3-031-57919-6_13

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  • DOI: https://doi.org/10.1007/978-3-031-57919-6_13

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  • Print ISBN: 978-3-031-57918-9

  • Online ISBN: 978-3-031-57919-6

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