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SaME: Sharpness-aware Matching Ensemble for Robust Palmprint Recognition

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13188))

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Abstract

Pose and illumination variations in unconstrained palmprint recognition cause critical problems in terms of region of interest (ROI) misalignment, defocus blur, and underexposured or overexposured imaging. However, most existing methods do not consider these quality factors when performing ROI matching; thus, palmprint recognition performance is sensitive to variations of palm poses and ambient light conditions. To address these problems, we propose the SaME strategy for robust contactless palmprint recognition. We have designed the sharpness-aware matching ensemble framework to exploit the advantages of different types of features while avoiding their limitations. First, we designed a quality scoring method based on an effective palmprint sharpness indicator. Second, a multi-feature extraction scheme was designed to take advantage of coarse-grained and fine-grained features. Finally, a quality-aware matching ensemble model is proposed to realize robust palmprint recognition. We conducted experiments on five contactless databases, and the results demonstrate that the proposed SaME framework can reduce the equal error rate (EER) significantly without complex ROI alignment. In addition, the EER value was less than 0.5% on the COEP\(\times 5\) dataset that was generated with considerable quality variations.

This work was supported in part by the NSFC under Grants 62176077 and 62172347; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019Bl515120055, in part by the Shenzhen Key Technical Project under Grant 2020N046, in part by the Shenzhen Fundamental Research Fund under Grant JCYJ20210324132210025, in part by the Open Project Fund (AC01202005018) from Shenzhen Institute of Artificial Intelligence and Robotics for Society, and in part by the Medical Biometrics Perception and Analysis Engineering Laboratory, Shenzhen, China.

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Notes

  1. 1.

    The Supplementary Material is available at https://github.com/xuliangcs/same.

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Authors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, 518055, China

    Xu Liang, Jinyang Yang, Guangming Lu & David Zhang

  2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China

    Dandan Fan & David Zhang

  3. The Chinese University of Hong Kong, Shenzhen, 518172, China

    Zhaoqun Li, Dandan Fan & David Zhang

Authors
  1. Xu Liang
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  2. Zhaoqun Li
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  3. Dandan Fan
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  4. Jinyang Yang
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  5. Guangming Lu
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  6. David Zhang
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Corresponding authors

Correspondence to Guangming Lu or David Zhang .

Editor information

Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Liang, X., Li, Z., Fan, D., Yang, J., Lu, G., Zhang, D. (2022). SaME: Sharpness-aware Matching Ensemble for Robust Palmprint Recognition. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_36

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  • DOI: https://doi.org/10.1007/978-3-031-02375-0_36

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  • Online ISBN: 978-3-031-02375-0

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