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Acta Optica Sinica, Volume. 38, Issue 2, 0215004(2018)

Palm Print and Palm Vein Joint Recognition System Based Video

Hao Wang, Wenxiong Kang*, and Xiaopeng Chen
Author Affiliations
  • School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (21)
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    Figures & Tables(13)
    Working flow chart of recognition system

    Fig. 1. Working flow chart of recognition system

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    Hardware model of recognition system. (a) Overall appearance; (b) diagram of internal structure

    Fig. 2. Hardware model of recognition system. (a) Overall appearance; (b) diagram of internal structure

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    Selected images chosen from rotating video

    Fig. 3. Selected images chosen from rotating video

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    Selected images chosen from sweeping video

    Fig. 4. Selected images chosen from sweeping video

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    Extraction steps of palm texture template. (a) Obtained palm vein image; (b) ROI of palm vein; (c) normalized ROI of palm vein; (d) enhanced image; (e) binary image

    Fig. 5. Extraction steps of palm texture template. (a) Obtained palm vein image; (b) ROI of palm vein; (c) normalized ROI of palm vein; (d) enhanced image; (e) binary image

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    Process of feature point detection. (a) ROI of palm print; (b) distribution schematic of fixed grids; (c) detected result of feature points

    Fig. 6. Process of feature point detection. (a) ROI of palm print; (b) distribution schematic of fixed grids; (c) detected result of feature points

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    Recognition flow charts of weighted fusion/SVM fusion and cascade fusion. (a) Weighted fusion/SVM fusion; (b) cascade fusion

    Fig. 7. Recognition flow charts of weighted fusion/SVM fusion and cascade fusion. (a) Weighted fusion/SVM fusion; (b) cascade fusion

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    Distribution curves of recognition performance. (a) Similarity score of template matching; (b) similarity score of MF_LBP matching; (c) EER

    Fig. 8. Distribution curves of recognition performance. (a) Similarity score of template matching; (b) similarity score of MF_LBP matching; (c) EER

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    Performance comparison of SIFT and FRDOH. (a) Recognition equal error rate; (b) computational efficiency

    Fig. 9. Performance comparison of SIFT and FRDOH. (a) Recognition equal error rate; (b) computational efficiency

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    Comparison of effects of score level fusion modes

    Fig. 10. Comparison of effects of score level fusion modes

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    • Table 1. Simulation results of template matching, MF_LBP matching and cascade fusion matching of palm vein

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      Table 1. Simulation results of template matching, MF_LBP matching and cascade fusion matching of palm vein

      MethodTemplate matchingMF_LBP matchingCascade fusion matching
      FRR /%26.0047.0011.78

    • Table 2. Comparison of recognition equal error rate under multi-frame feature fusion

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      Table 2. Comparison of recognition equal error rate under multi-frame feature fusion

      Register feature setRot+ Swp1Rot+ Swp2Rot+ Swp3RotSwp
      EER /%5.945.164.506.7311.40

    • Table 3. Time-consuming comparison of different feature extraction and matching

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      Table 3. Time-consuming comparison of different feature extraction and matching

      Recognition stepT1 feature extractionT2 feature extractionT3 feature extractionT1 matchingT2 matchingT3 matching
      Time /ms1752.512082011
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    Hao Wang, Wenxiong Kang, Xiaopeng Chen. Palm Print and Palm Vein Joint Recognition System Based Video[J]. Acta Optica Sinica, 2018, 38(2): 0215004

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    Paper Information

    Category: Machine Vision

    Received: Jul. 3, 2017

    Accepted: --

    Published Online: Aug. 30, 2018

    The Author Email: Kang Wenxiong (auwxkang@scut.edu.cn)

    DOI:10.3788/AOS201838.0215004

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology