Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010002(2021)

Finger Vein Recognition Based on Improved ResNet

Kaixuan Wang1、*, Guanghua Chen1,2, and Hongjia Chu1
Author Affiliations
  • 1Microelectronics R&D Center, Shanghai University, Shanghai 200444, China;
  • 2School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
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    Figures & Tables(9)
    Residual block
    Conventional convolution
    Depthwise convolution
    Depthwise over-parameterized convolution
    Structure of dual attention mechanism
    Improved residual block
    Improved ResNet
    Test accuracy. (a) FV-USM;(b) SDUMLA
    • Table 1. Comparison of structure and performance indicators of different models

      View table

      Table 1. Comparison of structure and performance indicators of different models

      MethodAccuracy /%Time /msParameter
      FV-USMSDUMLAFV-USMSDUMLA
      VGG-1695.833395.7721494944.0×106
      DenseNet96.951298.161834348.5×106
      AlexNet92.276496.3235161626.7×106
      ResNet-1896.036697.7941161619.7×106
      Improved ResNet+DO-Conv98.475698.7132151514.8×106
      Improved ResNet+DO-Conv+LSCE99.085498.8971151514.8×106
      Improved ResNet+DO-Conv+LSCE+(SE+SAM)99.491999.4485151514.8×106
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    Kaixuan Wang, Guanghua Chen, Hongjia Chu. Finger Vein Recognition Based on Improved ResNet[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010002

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    Paper Information

    Category: Image Processing

    Received: Nov. 30, 2020

    Accepted: Jan. 2, 2021

    Published Online: Oct. 12, 2021

    The Author Email: Wang Kaixuan (18361258215@163.com)

    DOI:10.3788/LOP202158.2010002

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