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[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 recognitionfeature extractionlocal binary patternstwo-dimensional principal componentbidirectional two-dimensional principal component analysiseuclidean distanceimage feature vectordimensionality 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.

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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