[1]胡娜,马慧,湛涛.融合LBP纹理特征与B2DPCA技术的手指静脉识别方法[J].智能系统学报,2019,14(03):533-540.[doi:10.11992/tis.201801014]
　HU Na,MA Hui,ZHAN Tao.Finger vein recognition method combining LBP texture feature and B2DPCA technology[J].CAAI Transactions on Intelligent Systems,2019,14(03):533-540.[doi:10.11992/tis.201801014]
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融合LBP纹理特征与B2DPCA技术的手指静脉识别方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
533-540
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
Finger vein recognition method combining LBP texture feature and B2DPCA technology
作者:
胡娜;  马慧;  湛涛
黑龙江大学 电子工程学院, 黑龙江 哈尔滨 150001
Author(s):
HU Na;  MA Hui;  ZHAN Tao
College of Electronic Engineering, Heilongjiang University, Harbin 150001, China
关键词:
手指静脉识别; 特征提取; LBP纹理特征; 二维主成分分析; 双向二维主成分分析; 欧氏距离; 图像特征向量; 降维
Keywords:
finger vein recognition; feature extraction; local binary patterns; two-dimensional principal component; bidirectional two-dimensional principal component analysis; euclidean distance; image feature vector; dimensionality reduction
分类号:
TP391.4
DOI:
10.11992/tis.201801014
摘要:
鉴于传统局部二进制模式 （local binary pattern， LBP） 算法对光照方向的变化非常敏感的问题，本文提出一种融合旋转不变模式的LBP算子与B2DPCA技术的手指静脉识别方法。首先提取手指静脉图像子块的LBP纹理谱特征，然后采用双向二维主成分分析方法对LBP特征向量构成的特征矩阵进行有效的降维处理，再通过比对降维后的待识别静脉图像特征向量与其他样本的特征向量之间的欧式距离来实现最终的样本分类。通过在天津市智能实验室静脉库及马来西亚理科大学FV-USM静脉库上进行实验验证，在不同训练样本数量下比较了8种算法的识别性能，相比于单一的LBP特征提取算法、经典降维算法和LBP与经典降维组合特征提取算法，该方法的识别率有很大的提高，证明了本文方法的有效性。
Abstract:
By considering the sensitivity of the traditional local binary pattern (LBP) algorithms while varying the illumination, this study proposes a finger vein recognition method using a rotation invariant LBP operator and B2DPCA. This method initially extracts the LBP texture spectrum feature of the image block of a finger vein, uses a bidirectional two-dimensional main component analysis method to effectively reduce the dimension of the eigenmatrix comprising the LBP eigenvectors, and finally classifies the final samples by comparing the Euclidean distance between the vein image eigenvectors that are to be identified and the eigenvectors of other samples after dimension reduction. The experiments were implemented on the finger vein image databases obtained from the Tianjin Intelligence Laboratory and from the FV-USM database of the University of Science, Malaysia. Further, eight methods with different numbers of training samples are compared, which exhibit that the fusion features that are proposed by this study perform considerably better than the single LBP operator, single traditional dimension-reduced methods, and the fusion of LBP and traditional dimension-reduced algorithms. Additionally, the recognition rate of the generated method was observed to significantly improve. This indicated that the analysis method proposed in this study is proper and effective.

参考文献/References:

[1] WALU? M, BERNACKI K, KONOPACKI J. Impact of NIR wavelength lighting in image acquisition on finger vein biometric system effectiveness[J]. Opto-electronics review, 2017, 25(4):263-268.
[2] 马慧, 王科俊. 采用旋转校正的指静脉图像感兴趣区域提取方法[J]. 智能系统学报, 2012, 7(3):230-234 MA Hui, WANG Kejun. A region of interest extraction method using rotation rectified finger vein images[J]. CAAI transactions on intelligent systems, 2012, 7(3):230-234
[3] WU Zhendong, TIAN Longwei, LI Ping, et al. Generating stable biometric keys for flexible cloud computing authentication using finger vein[J]. Information sciences, 2018, 433-434:431-447.
[4] YANG Jinfeng, SHI Yihu, JIA Guimin. Finger-vein image matching based on adaptive curve transformation[J]. Pattern recognition, 2017, 66:34-43.
[5] QIU Shirong, LIU Yaqin, ZHOU Yujia, et al. Finger-vein recognition based on dual-sliding window localization and pseudo-elliptical transformer[J]. Expert systems with applications, 2016, 64:618-632.
[6] ROSDI B A, SHING C W, SUANDI S A. Finger vein recognition using local line binary pattern[J]. Sensors, 2011, 11(12):11357-11371.
[7] HUI Ma, OLUWATOYIN P, SHULI S. A finger vein recognition method using improved oriented filter and elastic registration[J]. Research journal of applied sciences, engineering and technology, 2013, 6(7):1153-1159.
[8] LEE E C, PARK K R. Restoration method of skin scattering blurred vein image for finger vein recognition[J]. Electronics letters, 2009, 45(21):1074-1076.
[9] 王贺. 基于特征融合的手背静脉识别算法研究[D]. 吉林:吉林大学, 2017. WANG He. Research on dorsal hand vein recognition algorithm based on feature fusion[D]. Jilin:Jilin University, 2017.
[10] 杨文文, 毛建旭, 陈姜嘉旭. 基于分块LBP和分块PCA的指静脉识别方法[J]. 电子测量与仪器学报, 2016, 30(7):1000-1007 YANG Wenwen, MAO Jianxu, CHEN Jiangjiaxu. Finger vein recognition based on block LBP and block PCA[J]. Journal of electronic measurement and Instrumentation, 2016, 30(7):1000-1007
[11] GUPTA P, GUPTA P. An accurate finger vein based verification system[J]. Digital signal processing, 2015, 38:43-52.
[12] KANG Wenxiong, CHEN Xiaopeng, WU Qiuxia. The biometric recognition on contactless multi-spectrum finger images[J]. Infrared physics and technology, 2015, 68:19-27.
[13] 王科俊, 袁智. 基于小波矩融合PCA变换的手指静脉识别[J]. 模式识别与人工智能, 2007, 20(5):692-697 WANG Kejun, YUAN Zhi. Finger vein recognition based on wavelet moment fused with PCA transform[J]. Pattern recognition and artificial intelligence, 2007, 20(5):692-697
[14] 贾旭, 薛定宇, 崔建江, 等. 基于Contourlet子带能量特征多HMM融合的静脉识别[J]. 电子与信息学报, 2011, 33(8):1877-1882 JIA Xu, XUE Dingyu, CUI Jianjiang, et al. Vein recognition based on fusing multi HMMs with contourlet subband energy observations[J]. Journal of electronics and information technology, 2011, 33(8):1877-1882
[15] WANG Jiangang, YAU W Y, SUWANDY A, et al. Person recognition by fusing palmprint and palm vein images based on "Laplacianpalm" representation[J]. Pattern recognition, 2008, 41(5):1514-1527.
[16] YAN Xuekui, KANG Wenxiong, DENG Feiqi, et al. Palm vein recognition based on multi-sampling and feature-level fusion[J]. Neurocomputing, 2015, 151(2):798-807.
[17] QIN Huafeng, HE Xiping, YAO Xingyan, et al. Finger-vein verification based on the curvature in Radon space[J]. Expert systems with applications, 2017, 82:151-161.
[18] LI Peiqin, XIE Jianbin, LIU Tong, et al. Finger vein recognition algorithm based on optimized GHT[J]. Optik-international journal for light and electron optics, 2014, 125(6):1780-1783.
[19] 万源, 李欢欢, 吴克风, 等. LBP和HOG的分层特征融合的人脸识别[J]. 计算机辅助设计与图形学学报, 2015, 27(4):640-650 WAN Yuan, LI Huanhuan, WU Kefeng, et al. Fusion with Layered features of LBP and HOG for Face Recognition[J]. Journal of computer-aided design and computer graphics, 2015, 27(4):640-650
[20] ZHAO Guoying, AHONEN T, MATAS J, et al. Rotation-invariant image and video description with local binary pattern features[J]. IEEE transactions on image processing, 2012, 21(4):1465-1477.
[21] ZHANG Daoqiang, ZHOU Zhihua. (2D)2 PCA:two-directional two-dimensional PCA for efficient face representation and recognition[J]. Neurocomputing, 2005, 69(1/2/3):224-231.
[22] OJALA T, PIETIK?INEN M, M?ENP?? T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE transactions on pattern analysis and machine intelligence, 2002, 24(7):971-987.
[23] YANG Jian, ZHANG D, FRANGI A F, et al. Two-dimensional PCA:a new approach to appearance-based face representation and recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2004, 26(1):131-137.

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备注/Memo

备注/Memo:
收稿日期:2018-01-08。
基金项目:国家自然科学基金项目（61573132）；黑龙江省高校基本科研业务费项目（HDRCCX-201602）；黑龙江省高校重点实验室开放基金项目（DZGC201610）.
作者简介:胡娜,女,1991年生,硕士研究生,主要研究方向为模式识别、生物特征识别;马慧,女,1982年生,副教授,博士,主要研究方向为模式识别、生物特征识别。授权发明专利10余项。发表学术论文20余篇;湛涛,男,1992年生,硕士研究生,主要研究方向为模式识别、生物特征识别。
通讯作者:马慧.E-mail:2011043@hlju.edu.cn
更新日期/Last Update: 1900-01-01