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山东大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 103-109.doi: 10.6040/j.issn.1672-3961.0.2017.467

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一种基于加权图模型的手指静脉识别方法

叶子云1,杨金锋1,2*   

  1. 1. 中国民航大学电子信息与自动化学院, 天津 300300;2. 中国民航大学天津市智能信号与图像处理重点实验室, 天津 300300
  • 收稿日期:2017-09-22 出版日期:2018-06-20 发布日期:2017-09-22
  • 通讯作者: 杨金锋(1971—),男,河南淮阳人,工学博士,教授,主要研究方向为图像处理,生物识别,计算机视觉. E-mail: jfyang@cauc.edu.cn E-mail:297496156@qq.com
  • 作者简介:叶子云(1992— ),女,福建政和人,硕士研究生,主要研究方向为图像处理,生物特征识别. E-mail: 297496156@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61379102,U1433120,61502498);中央高校基本科研业务费专项资金资助项目(3122017001)

A finger-vein recognition method based on weighted graph model

YE Ziyun1, YANG Jinfeng1,2*   

  1. 1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;
    2. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China
  • Received:2017-09-22 Online:2018-06-20 Published:2017-09-22

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摘要： 提出一种基于加权图模型的手指静脉网络特征描述方法。对于一幅手指静脉图像,通过图像划分获得图的顶点集,利用三角剖分获得图的边集,边的权重由边所连接顶点之间的特征相似度决定。通过这种方式,一幅手指静脉图像可转化为一个加权图,并通过度量加权图邻接矩阵之间的相似度实现手指静脉识别。详细研究影响识别结果的几个因素,并通过试验证明了该方法的有效性。

关键词: 加权图, 特征提取, 手指静脉识别, 图论

Abstract: A new weighted graph construction method was proposed for finger-vein network representation. For a weighted graph, its nodes and edges were respectively generated by dividing image into blocks and a triangulation algorithm, and the weights of edges were valued using the feature similarities between adjacent blocks. In this way, a finger-vein image could be represented by a weighted graph, and the adjacency matrix of this weighted graph was used for finger-vein recognition. The experiment results proved the effectiveness of the method, and some important factors that affected graph recognition results were discussed in detail.

Key words: finger-vein recognition, feature extraction, graph theory, weighted graph structure

中图分类号: 

  • TP391
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