摘要
为了有效提取手背静脉纹理特征,并对其进行识别匹配,提出了一种基于优化Gabor核纹理特征的手背静脉识别系统。首先,设计了一套手背静脉采集装置采集静脉图像。然后,对采集到的手背静脉图像预处理后进行三层Haar小波分解,再使用不同尺度和方向的Gabor核提取低频子带图像的纹理特征,之后使用主成分分析(PCA)法对纹理特征进行降维。最后,采用基于欧式距离的最近邻分类器进行识别。本文通过采集装置建立了具有较高质量的吉林大学手背静脉图像数据库,并在其上对本系统进行了性能测试。整体实验结果表明:该手背静脉识别系统能有效提高特征的识别速度,同时可达到98.5%的识别准确率,具有一定的应用前景。
In order to extract and match the texture features of dorsal hand veto,this paper presents a dorsal hand vein recognition system based on optimized texture features.First,we design a image acquisition device to collect vein images.Then,after image preprocessing and three-layer haar wavelet decomposition,we use different scales and directions of gabor kernel function to extract texture features of low-frequency sub-band images.Finally,PCA is used tO reduce the dimensionality of features and the nearest neighbor classifier based on Euclidean distance is used to match the features. In this paper,a dorsal hand vein database of Jilin University is established.The experimental results show that the proposed recognition system can effectively improve the identification speed of features and the recognition rate can reach 98.5%.Good efforts on recognition accuracy and efficiency are achieved by the system.
作者
刘富
宗宇轩
康冰
张益萌
林彩霞
赵宏伟
LIU Fu;ZONG Yu-xuan;KANG Bing;ZHANG Yi-meng;LIN Cai-xia;ZHAO Hong-wei(National Key Laboratory for Automotive Simulation and Control,Jilin University,Changchun 130022,China;College of Communication Engineering,Jilin University,Changchun 130022,China;Electronic and Electrical Engineering Department,The University of Sheffield,Sheffield,S102TN ,UK;College of Information Science and Technology,Hainan University,Haikou 570228,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2018年第6期1844-1850,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(61503151)
吉林省自然科学基金项目(10100505)
吉林省产业创新专项资金项目(2017C032-4,3J117R015420)
海南省自然科学基金面上项目(0112631040).
关键词
计算机应用
手背静脉识别系统
纹理特征优化
手背静脉采集装置
computer application
dorsal hand vein recognition system
texture feature optimization
vein acquisition device
