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  • Received: Jul. 26, 2019

    Accepted: Sep. 6, 2019

    Posted: Apr. 1, 2020

    Published Online: Apr. 3, 2020

    The Author Email: Hu Yalei (mrhu165981@163.com)

    DOI: 10.3788/LOP57.081005

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    Zhiyong Tao, Yalei Hu, Sen Lin. Finger Vein Recognition Based on Improved AlexNet[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081005

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Laser & Optoelectronics Progress, Vol. 57, Issue 8, 081005 (2020)

Finger Vein Recognition Based on Improved AlexNet

Tao Zhiyong1, Hu Yalei1,2,*, and Lin Sen1

Author Affiliations

  • 1School of Electronic & Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China;
  • 2Fuxinlixing Technology Company Limited, Fuxin, Liaoning 123000, China

Abstract

An improved AlexNet structure is proposed to solve the problem of long time and low recognition accuracy of an AlexNet training finger vein recognition system. To address the problem of limited image size and poor adaptability of an AlexNet network model, the network structure of spatial pyramid pooling mode is introduced. To fasten the network’s training speed and reduce the complexity of the network model, the convolution kernel size of AlexNet, network depth, and the full connection layer are adjusted. Results show that the improved network model has a significant improvement on the recognition accuracy and training duration compared with the AlexNet model in both public and private finger vein datasets.

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