一种基于图模型与定序编码的手指静脉识别方法
Weighted Graph and Gabor Ordinal Measure Based Description for Finger-Vein Network
DOI: 10.12677/CSA.2021.1112298, PDF,   
作者: 叶子云, 石滨萌, 温梦娜*：深圳职业技术学院粤港澳大湾区人工智能应用技术研究院，广东 深圳
关键词: 手指静脉识别；加权图；定序编码；Finger-Vein Recognition； Weighted Graph； Gabor Ordinal Measure
摘要: 手指静脉的网络结构是指静脉区分性的来源，但获得可靠的手指静脉网络结构描述一直是个难题。为此，本文提出一种基于图模型与定序编码的手指静脉网络特征描述方法。对于一幅手指静脉图像，首先通过划分图块来获得图的节点集，其次利用三角剖分法获得图的边集，边的权重由边所连接的节点特征来决定。经过上述操作，一幅手指静脉图像可构建一个加权图，通过度量加权图的邻接矩阵相似度来实现手指静脉识别。本文中研究影响识别结果的几个因素，并通过实验证明了该方法的有效性。
Abstract: The network structure of finger veins is the source of distinguishment, but it has always been a difficult problem to obtain a reliable description of the vein network structure. So, in this paper, we propose a finger vein network feature description method based on graph model and Gabor ordinal measure. For a finger vein image, this paper first obtains the node-set of the graph by block division, and then uses the triangulation method to obtain the edge-set of the graph. The weights of the edges are determined by the features of the nodes connected by the edges. After the above operations, a finger-vein image could be represented by a weighted graph, and the adjacency matrix of this weighted graph was used for fingervein recognition. In this paper, several factors affecting the recognition results are studied, and the effectiveness of the method is proved through experiments.
文章引用：叶子云, 石滨萌, 温梦娜. 一种基于图模型与定序编码的手指静脉识别方法[J]. 计算机科学与应用, 2021, 11(12): 2945-2952. https://doi.org/10.12677/CSA.2021.1112298

参考文献

[1] Yang, J.F. and Shi, Y.H. (2012) Finger-Vein ROI Localization and Vein Ridge Enhancement. Pattern Recognition Let-ters, 33, 1569-1579. [Google Scholar] [CrossRef] 
[2] Ojala, T., Pietikainen, M. and Harwood, D. (1996) A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern Recogni-tion, 29, 51-59. [Google Scholar] [CrossRef] 
[3] Ojala, T., Pietikainen, M. and Maenpaa, T. (2002) Multi-Resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transaction on Pattern Analysis and Machine Intelligence, 24, 971-987. [Google Scholar] [CrossRef] 
[4] Zhang, L., Zhang, L., Zhang, D., et al. (2010) Online Fin-ger-Knuckle-Print Verification for Personal Authentication. Pattern Recognition, 43, 2560-2571. [Google Scholar] [CrossRef] 
[5] Sun, Z.N. and Tan, T.N. (2009) Ordinal Measures for Iris Recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, 31, 2211-2226. [Google Scholar] [CrossRef] 
[6] Chai, Z.H., Sun, Z.N., Vazquez, H., et al. (2013) Gabor Ordinal Measures for Face Recognition. IEEE Transactions on Information Forensics & Security, 9, 14-26. [Google Scholar] [CrossRef] 
[7] West, D. 图论导引[M]. 李建中, 骆吉洲, 译. 北京: 机械工业出版社, 2006.
[8] Luo, B., Wilson, R.C. and Hancock, E.R. (2003) Spectral Embedding of Graphs. Pattern Recog-nition, 36, 2213-2230. [Google Scholar] [CrossRef] 
[9] 汤进, 翟素兰, 罗斌. 基于加权关系图谱特征的图像检索[J]. 计算机工程, 2007, 33(24): 19-21.
[10] 汤进. 基于图理论的图像描述与检索方法研究[D]: [博士学位论文]. 合肥: 安徽大学, 2007.
[11] Choi, G., Lim, C.G. and Choi, H.J. (2017) A Center-Biased Graph Learning Algorithm for Image Classification. In: Choi, H., Peng, W.C. and Sakurai, Y., Eds., IEEE International Conference on Big Data and Smart Computing, Jeju Island, 13-16 February 2017, 324-327.
[12] Bashier, H.K., Abusham, E.A. and Khalid, F. (2012) Face Detection Based on Graph Structure and Neural Networks. Trends in Applied Sciences Research, 7, 75-79. [Google Scholar] [CrossRef] 
[13] Abusham, E.A. and Bashir, H.K. (2011) Face Recognition Using Local Graph Structure (LGS). In: Jacko, J.A., Ed., International Conference on Human-Computer Interaction, Orlando, 9-14 July 2011, 169-175. [Google Scholar] [CrossRef] 
[14] Dong, S., Yang, J., Chen, Y., et al. (2015) Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure. Ksii Transactions on Internet & Information Systems, 9, 4126-4142. [Google Scholar] [CrossRef] 
[15] Horadam, K.J., Davis, S.A., Arakala, A., et al. (2012) Fingerprints as Spatial Graphs: Nodes and Edges. In: Bradley, A.P. and Jackway, P.T., Eds., International Conference on Digital Image Computing Techniques and Applications, Fremantle, 3-5 December 2012, 400-405. [Google Scholar] [CrossRef] 
[16] Lajevardi, S.M., Arakala, A., Davis, S.A., et al. (2013) Retina Veri-fication System Based on Biometric Graph Matching. IEEE Transactions on Image Processing, 22, 3625-3635. [Google Scholar] [CrossRef] 
[17] Arakala, A., Davis, S.A., Hao, H., et al. (2017) Value of Graph Topology in Vascular Biometrics. IET Biometrics, 6, 117-125. [Google Scholar] [CrossRef] 
[18] La-jevardi, S.M. (2014) Hand Vein Authentication Using Biometric Graph Matching. IET Biometrics, 3, 302-313. [Google Scholar] [CrossRef] 
[19] Sun, Z., Tan, T., Wang, Y., et al. (2005) Ordinal Palmprint Repre-sention for Personal Identification. In: Agrawal, M., et al., Eds., IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-25 June 2005, 279-284.
[20] Liao, S., Lei, Z., Zhu, X., et al. (2006) Face Recognition Using Ordinal Features. In: Zhang, D. and Jain, A.K., Eds., Advances in Biometrics, Springer, Berlin, 40-46. [Google Scholar] [CrossRef]