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基于线特征韦伯局部描述子的掌纹识别 被引量:2

Palmprint recognition method based on line feature Weber local descriptor
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摘要 目的将韦伯局部描述子WLD(Weber local descriptor)应用于掌纹识别,并针对掌纹具有丰富线特征的特点,在WLD基础上改进获得线特征韦伯局部描述子LWLD(1ine feature weber local descriptor),以提高掌纹识别的效率。方法首先采用MFRAT或Gabor滤波器对掌纹图像进行线性滤波,生成方向图和能量图;然后对能量进行韦伯差分激励滤波生成差分激励图;最后,基于方向图和差分激励图构造线韦伯特征直方图,并基于线韦伯特征直方图进行掌纹特征识别。结果基于Poly Ⅱ和Cross—Sensor掌纹库进行对比实验,采用曼哈顿距离和卡方距离进行匹配,其中在PolyaⅡ库上的识别率最高均达到100%,在识别率和容错性方面均优于其他主要基于局部描述子的识别方法。结论首次将韦伯局部描述子引入掌纹识别领域,发展了一种新的基于局部描述子的掌纹识别方法。和其他基于局部描述子的掌纹识别算法相比,本文方法具有更高识别率和稳定性。 Objective Given the advantages of low computation cost and absence of a training requirement, local descriptor- based palmprint recognition methods are eliciting an increasing amount of attention. The Weber local descriptor (WLD) is a newly presented local descriptor inspired by Weber' s law in psychology. This study applies WLD to palmprint recogni- tion. To improve palmprint recognition performance, a line feature WLD is presented by considering the sufficient line fea- tures of a palmprint. Method First, modified finite random transform or the Gabor filter is applied to a palmprint image to generate directional image q~ and energy image E. Second, energy image E is convoluted by the Weber operator to generate differential excitation image. Finally, based on directional image and differential excitation image , the histogram of the line Weber local feature can be constructed for use in palmprint recognition. Result The Polytechnic University Palmprint Database Ⅱ and the Cross-Sensor Palmprint Database are utilized in an experiment on Polyu Ⅱ database. The proposed method can achieve 100% identification rate with both Manhattan and chi-square distance. Results demonstrate that the presented method has a high identification rate and is robust. Conclusion This study introduces WLD into palmprint recog- nition to develop a new palmprint recognition method based on a local descriptor. Compared with other palmprint recognition methods based on a local descriptor, the presented method has a higher identification rate and is more robust.
作者 罗月童 赵兰英 贾伟 顾靖 薛峰 Luo Yuetong , Zhao Lanying , Jia Wei, Gu Jing , Xue Feng( 1. Institute of Visualization & Cooperative Computing, Hefei University of Technology, Hefei 230009, China ; 2. Itefci Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)
出处 《中国图象图形学报》 CSCD 北大核心 2016年第2期235-244,共10页 Journal of Image and Graphics
基金 国家自然科学基金项目(61202283,61472115,61370167,61305093) 安徽省科技攻关项目(1401b042009)
关键词 机器学习 生物特征 掌纹识别 韦伯局部描述子 线特征 machine learning biological characteristics palmprint recognition Weber local descriptor line feature
作者简介 罗月童(1978-),男,教授,2005年于合肥工业大学获计算机应用专业博士学位,主要研究方向为科学计算可视化、计算机辅助设计、生物特征识别和计算机技术在核能领域的应用。E—mail:yfluo@hfut.edu.cn 通信作者:贾伟,副教授,E-mail:china.jiawei@139.com
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