[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(3):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(3):533-540.[doi:10.11992/tis.201801014]
点击复制
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(3):533-540.[doi:10.11992/tis.201801014]
融合LBP纹理特征与B2DPCA技术的手指静脉识别方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第3期
页码:
533-540
栏目:
学术论文—机器感知与模式识别
出版日期:
2019-05-05
- Title:
- Finger vein recognition method combining LBP texture feature and B2DPCA technology
- 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
- 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.
- 相似文献/References:
-
[1]黄剑华,唐降龙,刘家锋,等.一种基于Homogeneity的文本检测新方法[J].智能系统学报,2007,2(1):69.
HUANG Jian-hua,TANG Xiang-long,LIU Jia-feng,et al.A new method for text detection based on Homogeneity[J].CAAI Transactions on Intelligent Systems,2007,2():69.
[2]谭 营,朱元春.反垃圾电子邮件方法研究进展[J].智能系统学报,2010,5(3):189.
TAN Ying,ZHU Yuan-chun.Advances in antispam techniques[J].CAAI Transactions on Intelligent Systems,2010,5():189.
[3]王斐,张育中,宁廷会,等.脑-机接口研究进展[J].智能系统学报,2011,6(3):189.
WANG Fei,ZHANG Yuzhong,NING Tinghui,et al.Research progress in a braincomputer interface[J].CAAI Transactions on Intelligent Systems,2011,6():189.
[4]刘琚,孙建德.独立分量分析的图像/视频分析与应用[J].智能系统学报,2011,6(6):495.
LIU Ju,SUN Jiande.Independent component analysisbased image/video analysis and applications[J].CAAI Transactions on Intelligent Systems,2011,6():495.
[5]谭营,王军.手指静脉身份识别技术最新进展[J].智能系统学报,2011,6(6):471.
TAN Ying,WANG Jun.Recent advances in finger vein based biometric techniques[J].CAAI Transactions on Intelligent Systems,2011,6():471.
[6]吴家伟,严京旗,方志宏,等.基于图像显著性特征的铸坯表面缺陷检测[J].智能系统学报,2012,7(1):75.
WU Jiawei,YAN Jingqi,FANG Zhihong,et al.Defect detection on a steel slab surface based on the characteristics of an image’s saliency region[J].CAAI Transactions on Intelligent Systems,2012,7():75.
[7]张毅,罗明伟,罗元.脑电信号的小波变换和样本熵特征提取方法[J].智能系统学报,2012,7(4):339.
ZHANG Yi,LUO Mingwei,LUO Yuan.EEG feature extraction method based on wavelet transform and sample entropy[J].CAAI Transactions on Intelligent Systems,2012,7():339.
[8]刘忠宝,王士同.从Parzen窗核密度估计到特征提取方法：新的研究视角[J].智能系统学报,2012,7(6):471.
LIU Zhongbao,WANG Shitong.From Parzen window estimation to feature extraction: a new perspective[J].CAAI Transactions on Intelligent Systems,2012,7():471.
[9]孙倩茹,王文敏,刘宏.视频序列的人体运动描述方法综述[J].智能系统学报,2013,8(3):189.
SUN Qianru,WANG Wenmin,LIU Hong.Study of human action representation in video sequences[J].CAAI Transactions on Intelligent Systems,2013,8():189.
[10]许可乐,唐涛,蒋咏梅.一种SAR图像稳健特征点提取方法[J].智能系统学报,2013,8(4):287.[doi:10.3969/j.issn.1673-4785.201304038]
XU Kele,TANG Tao,JIANG Yongmei.A stable feature point extraction approach for SAR image registration[J].CAAI Transactions on Intelligent Systems,2013,8():287.[doi:10.3969/j.issn.1673-4785.201304038]
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