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基于曲面类型与深度学习融合的三维掌纹识别技术

张宗华 王晟贤 高楠 孟召宗

张宗华, 王晟贤, 高楠, 孟召宗. 基于曲面类型与深度学习融合的三维掌纹识别技术[J]. 电子与信息学报, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982
引用本文: 张宗华, 王晟贤, 高楠, 孟召宗. 基于曲面类型与深度学习融合的三维掌纹识别技术[J]. 电子与信息学报, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982
ZHANG Zonghua, WANG Shengxian, GAO Nan, MENG Zhaozong. Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982
Citation: ZHANG Zonghua, WANG Shengxian, GAO Nan, MENG Zhaozong. Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982

基于曲面类型与深度学习融合的三维掌纹识别技术

doi: 10.11999/JEIT200982
基金项目: 国家自然科学基金(52075147, 51675160),重大科学仪器设备开发重点专项(2017YFF0106404)
详细信息
    作者简介:

    张宗华:男,1974年生,教授,研究方向为光学检测、3维数字成像和造型、条纹自动分析、生物特征识别等

    王晟贤:男,1995年生,硕士生,研究方向为光学检测、深度学习、生物特征识别

    高楠:男,1982年生,副教授,研究方向为光学测量与光谱检测

    孟召宗:男,1983年生,副教授,研究方向为新型感知计算网络、光学3维测量、工业过程信息化与智能化

    通讯作者:

    张宗华 zhzhang@hebut.edu.cn

  • 中图分类号: TN911.73; TP391.4

Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning

Funds: The National Natural Science Foundation of China (52075147, 51675160), The National Key R&D Program of China (2017YFF0106404)
  • 摘要: 传统的2维掌纹识别在图像采集时容易受到干湿度、残影和压力等影响,使得其鲁棒性和准确性降低。为解决这些问题,3维掌纹识别技术应运而生。现有的3维掌纹身份认证技术需要将掌纹的特征提取与匹配识别分开进行,不仅延缓了识别时间,更增加了不同方法优化组合的难度。该文提出一种基于曲面类型(ST)与深度学习融合的3维掌纹识别方法。该方法利用ST图像表示3维掌纹特征,并将其作为卷积神经网络(CNN)的输入,实现网络的训练。测试图像可自行提取掌纹图像特征信息并在网络中直接完成识别。实验结果表明,该文方法在公开数据集上得到了99.43%的准确率和28 ms的识别时间,与传统3维掌纹识别方法相比均有提高,实现了3维掌纹的快速高精度识别。
  • 图  1  网络整体框架图

    图  2  所提方法流程图

    图  3  3维掌纹ST的深度学习结果

    表  1  由曲率得到的9类ST

    $K > 0$$K = 0$$K < 0$
    $H < 0$
    (ST=1)

    (ST=2)
    鞍岭
    (ST=3)
    $H = 0$
    (ST=4)
    平坦
    (ST=5)
    低点
    (ST=6)
    $H > 0$
    (ST=7)

    (ST=8)
    鞍谷
    (ST=9)
    下载: 导出CSV

    表  2  2维掌纹及3维掌纹不同特征表示方法的对比实验结果

    不同特征识别率(%)识别时间(ms)
    2维掌纹ROI94.7529
    GCI95.5330
    CST98.8828
    MCI99.0529
    ST99.4328
    下载: 导出CSV

    表  3  不同网络的对比实验结果

    网络识别率(%)训练时间(ms)识别时间(ms)
    LeNet+ST78.8514200031
    AlexNet+ST[11]99.4015100035
    改进LeNet5+ST99.4312800028
    下载: 导出CSV

    表  4  不同方法的对比实验

    方法识别率(%)
    MCI运算[18]90.53
    GCI运算[18]82.83
    ST差运算[18]98.58
    ST稀疏矩阵[19]99.15
    分块ST+PCA[20]99.25
    本文方法99.43
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-18
  • 修回日期:  2022-01-19
  • 录用日期:  2022-02-16
  • 网络出版日期:  2022-02-22
  • 刊出日期:  2022-04-18

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