• Laser & Optoelectronics Progress
  • Vol. 57, Issue 8, 081005 (2020)
Zhiyong Tao1, Yalei Hu1、2、*, and Sen Lin1
Author Affiliations
  • 1School of Electronic & Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China;
  • 2Fuxinlixing Technology Company Limited, Fuxin, Liaoning 123000, China
  • show less
    DOI: 10.3788/LOP57.081005 Cite this Article Set citation alerts
    Zhiyong Tao, Yalei Hu, Sen Lin. Finger Vein Recognition Based on Improved AlexNet[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081005 Copy Citation Text show less
    Image collection method. (a) Direct light collection; (b) light reflection collection
    Fig. 1. Image collection method. (a) Direct light collection; (b) light reflection collection
    Finger vein pretreatment process
    Fig. 2. Finger vein pretreatment process
    Gray scale linear interpolation schematic
    Fig. 3. Gray scale linear interpolation schematic
    Finger vein effect after CLAHE algorithm processing. (a) Original image; (b) image enhancement
    Fig. 4. Finger vein effect after CLAHE algorithm processing. (a) Original image; (b) image enhancement
    AlexNet model structure
    Fig. 5. AlexNet model structure
    Schematic of SPP
    Fig. 6. Schematic of SPP
    Im-AlexNet model structure renderings
    Fig. 7. Im-AlexNet model structure renderings
    Curves of JY_DB. (a) Loss curves; (b) recognition accuracy curves
    Fig. 8. Curves of JY_DB. (a) Loss curves; (b) recognition accuracy curves
    Curves of SD_DB. (a) Loss curves; (b) recognition accuracy curves
    Fig. 9. Curves of SD_DB. (a) Loss curves; (b) recognition accuracy curves
    LayerNumber of filtersSize of kernelStride
    Conv19611×114
    Pool1-3×32
    Conv22565×51
    Pool2-3×32
    Conv33843×31
    Conv43843×31
    Conv52563×31
    Pool3-3×32
    FC layer140961×11
    FC layer240961×11
    FC layer310001×11
    Table 1. AlexNet model structure parameters
    LayerNumber of filtersSize of kernelStride
    Conv1965×54
    Conv2961×11
    Pool1-3×32
    Conv32565×51
    Conv42561×11
    Pool2-3×32
    Conv53843×31
    Conv63841×11
    Conv73843×31
    Conv83841×11
    Conv92563×31
    Conv102561×11
    Pool3(SPP)---
    FC layer110241×11
    FC layer2Class1×11
    Table 2. Im-AlexNet model structure parameters
    Table 3. Finger vein datasets division
    ParameterValue
    Batch128
    Epoch500
    Numbers of samples per epoch1280
    LR of SGD0.001
    Momentum of SGD0.9
    Training number64000
    Dropout0.25
    Table 4. Experimental parameters
    NetworkDatabaseTime/min
    AlexNetJY_DB125
    SD_DB84
    Im-AlexNetJY_DB29
    SD_DB25
    Table 5. Comparison of training time before and after network improvement
    NetworkDatabaseAccuracy/%
    AlexNetJY_DB95.04
    SD_DB94.36
    Im-AlexNetJY_DB99.25
    SD_DB98.23
    Table 6. Comparison of recognition accuracy before and after network improvement
    MethodAccuracy /%
    SPF[24]87.00
    SPCF[25]92.71
    CLAHE+directional dilation (DD) [26]90.72
    CNN (ULDFV-VGG16[15])92.60
    CNN (ULDFV-Xception[15])93.50
    CNN (ULDFV-ResNet[15])96.60
    CNN (Inception-ResNet V2[15])96.70
    Dula-sliding window+location+Pseudo-elliptical transformer+2D-PCA[27]97.02
    Block-based average absolute deviation (AAD) features[28]97.76
    CNN (Proposed CNN[29])97.48
    CNN (AlexNet)94.36
    CNN (Im-AlexNet)98.23
    Table 7. Comparison of recognition accuracy of different image feature algorithms on SD_DB
    Zhiyong Tao, Yalei Hu, Sen Lin. Finger Vein Recognition Based on Improved AlexNet[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081005
    Download Citation