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基于细节点邻域信息的可撤销指纹模板生成算法

许秋旺 张雪锋

许秋旺, 张雪锋. 基于细节点邻域信息的可撤销指纹模板生成算法. 自动化学报, 2017, 43(4): 645-652. doi: 10.16383/j.aas.2017.c160069
引用本文: 许秋旺, 张雪锋. 基于细节点邻域信息的可撤销指纹模板生成算法. 自动化学报, 2017, 43(4): 645-652. doi: 10.16383/j.aas.2017.c160069
XU Qiu-Wang, ZHANG Xue-Feng. Generating Cancelable Fingerprint Templates Using Minutiae Local Information. ACTA AUTOMATICA SINICA, 2017, 43(4): 645-652. doi: 10.16383/j.aas.2017.c160069
Citation: XU Qiu-Wang, ZHANG Xue-Feng. Generating Cancelable Fingerprint Templates Using Minutiae Local Information. ACTA AUTOMATICA SINICA, 2017, 43(4): 645-652. doi: 10.16383/j.aas.2017.c160069

基于细节点邻域信息的可撤销指纹模板生成算法

doi: 10.16383/j.aas.2017.c160069
基金项目: 

国家自然科学基金 61301091

详细信息
    作者简介:

    许秋旺 西安邮电大学通信与信息工程学院硕士研究生.主要研究方向为信息安全.E-mail:xuqiuwang@126.com

    通讯作者:

    张雪锋 博士, 西安邮电大学通信与信息工程学院教授.主要研究方向为信息安全.E-mail:zhangxuefeng3@163.com

Generating Cancelable Fingerprint Templates Using Minutiae Local Information

Funds: 

National Natural Science Foundation of China 61301091

More Information
    Author Bio:

    Master student at the School of Communication and Information Engineering, Xi0an University of Posts and Telecommunications. His main research interest is information security

    Corresponding author: ZHANG Xue-Feng Ph. D., professor at the School of Communication and Information Engineering, Xi0an University of Posts and Telecommunications. His main research interest is information security. Corresponding author of this paper
  • 摘要: 为了提高指纹模板算法的安全性等性能,设计了一种基于细节点邻域信息的可撤销指纹模板生成算法.首先对指纹图像进行预处理,提取指纹的细节点特征,然后采用改进的细节点描述子采样结构提取细节点邻域的纹线特征,最后结合用户PIN码生成指纹模板,同时结合贪婪算法设计了相应的指纹匹配算法.在指纹数据库FVC2002-DB1和DB2上的实验表明,该算法具有良好的认证性能,能较好地满足可撤销性、多样性和不可逆性,而且改进的采样结构在没有降低系统识别性能的情况下,进一步拓展了细节点描述子的采样结构方式.
    1)  本文责任编委 封举富
  • 图  1  quad细节点描述子中采样点的分布结构

    Fig.  1  quad Sampling points structure of the minutia descriptor

    图  2  基于细节点邻域信息的可撤销模板生成流程图

    Fig.  2  Process diagram of proposed method for template generation

    图  3  查询模板与注册模板的匹配过程 (d (θ) 为纹理串元素)

    Fig.  3  Similarity score between query and enrolled fingerprint template (d (θ) is real value)

    图  4  用户PIN码安全时真假匹配的分布情况

    Fig.  4  Genuine and imposter distributions in safe-PIN scenario

    图  5  用户PIN码被盗后真假匹配的分布情况

    Fig.  5  Genuine and imposter distributions in stolen-PIN scenario

    图  6  在用户PIN码被盗情形下, Tico细节点描述子结构与本文方法的ROC曲线

    Fig.  6  ROC curves of Tico sampling structure and proposed method for the stolen-PIN

    图  7  相同PIN码的EER曲线 (FVC2002-DB1)

    Fig.  7  EER of FVC2002-DB1 for the same PIN

    图  8  相同PIN码的EER曲线 (FVC2002-DB2)

    Fig.  8  EER of FVC2002-DB2 for the same PIN

    图  9  在FVC2002-DB1数据库中, 分别使用相同PIN码和不同PIN码的真、假匹配分数分布情况

    Fig.  9  Genuine, imposter and pseudo-imposter distributions for FVC2002-DB1

    图  10  在FVC2002-DB2数据库中, 分别使用相同PIN码和不同PIN码的真、假匹配分数分布情况

    Fig.  10  Genuine, imposter and pseudo-imposter distributions for FVC2002-DB2

    表  1  FVC2002-DB1和FVC2002-DB2数据库参数

    Table  1  Summary of databases used in our experiments

    FVC2002-DB1 FVC2002-DB2
    采集设备 Touch View Ⅱ FX2000
    光学采集仪光学采集仪
    图像尺寸388 × 374296 × 560
    手指数量100100
    每枚手指采集次数88
    分辨率500 dpi596 dpi
    图像质量 Good Medium
    下载: 导出CSV

    表  2  不同的采样圆环r与采样点个数K下的匹配结果

    Table  2  EER of different sampling point structure around each minutiae for the same PIN (%)

    采样点构造
    (r1, K1), (r2, K2), …, (rL, KL)
    FVC2002-DB1
    EER
    FVC2002-DB2
    EER
    (42, 14), (60, 20), (78, 26) 8.12 7.33
    (42, 14), (60, 20), (78, 26), (93, 32)17.3721.18
    (27, 10), (45, 16), (63, 22), (81, 28)12.1411.54
    (12, 14), (24, 18), (36, 24), (48, 28) 3.26 4.58
    下载: 导出CSV

    表  3  不同方法的性能对比 (EER)(%)

    Table  3  EER comparison between proposed method and some existing methods (%)

    方法 FVC2002-DB1 FVC2002-DB2
    Lee和Kim[12] 10.30 9.50
    Ahmad等[14] 9.00 6.00
    Jin等[13] 5.19 5.65
    Wang和Hu[21] 3.50 5.00
    Belguechi等[22] 3.78 6.68
    本文方法 3.26 4.58
    下载: 导出CSV

    表  4  采用均匀采样方法和本文提出方法的性能比较 (EER) (%)

    Table  4  EER comparison between the method of uniform sampling structure and proposed method (%)

    方法 用户PIN码安全 用户PIN码泄露
    DB1 DB2 DB1 DB2
    均匀采样 0 0 4.97 6.31
    本文方法 0.02 0 3.26 4.58
    下载: 导出CSV

    表  5  真、假匹配分布的均值与方差

    Table  5  The mean and standard deviations of the distributions

    指纹数据库 相同PIN码的真匹配 相同PIN码的假匹配 不同PIN码的假匹配 伪假匹配
    均值 方差 均值 方差 均值 方差 均值 方差
    FVC2002-DB1 0.8940 1.24 × 10-3 0.7624 1.18 × 10-3 0.6186 4.31 × 10-4 0.6254 4.97 × 10-4
    FVC2002-DB2 0.8860 1.30 × 10-3 0.7731 6.23 × 10-4 0.6312 1.98 × 10-4 0.6294 3.52 × 10-4
    下载: 导出CSV

    表  6  不同方法的假匹配分布的均值与方差 (FVC2002-DB2)

    Table  6  The mean and standard deviations of the different methods imposter distributions for FVC2002-DB2

    方法 不同PIN码的假匹配 伪假匹配
    均值 方差 均值 方差
    Lee和Kim[12] 0.0470 6.83× 10-5 0.0480 1.14× 10-4
    Jin等[13] 0.05970.0143 0.05420.0228
    Wang和Hu[21] 0.28170.0208 0.28970.0057
    本文方法 0.6312 1.98× 10-4 0.6294 3.52× 10-4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-01-22
  • 录用日期:  2016-04-09
  • 刊出日期:  2017-04-01

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