摘要
手写签名作为易被大众所接受的生物特征身份认证方式,已成为模式识别领域一个重要研究热点。针对现有手写签名存在易模仿难鉴定的问题,提出一种结合演化超网络模型的手写签名认证方法。为了平滑噪声,构造出可读性强的笔迹特征集,采用向量化和平滑采集点的方法对手写签名样本进行预处理,从而提取出位置和方向特征属性,采用演化超网络模型对签名进行学习和鉴定。为验证该方法的有效性,对20个签名用户分别采集了40个真实签名和20个伪造签名数据进行实验。实验结果表明,该方法对用户签名的误拒率(false rejection rate,FRR)为4.75%,误纳率(false acceptance rate,FAR)为3.75%,识别率(verification accuracy,VA)为95.75%。同时和其他传统的识别算法相比,具有更高的识别率。
Handwritten signature is one of the most widely accepted biometric verification technologies and it has become an important research focus in the field of pattern recognition. Handwritten signature is a challenging task because of the easi-ness of forging and difficulty of identifying one’s signature. As for these problems,an evolutionary hypernetwork model for handwritten verification is proposed in this paper. In order to smooth noise and construct the more readable handwritten fea-tures,the position of points and direction are extracted as features through the vectoring and smoothing as pre-processing.Then,an evolutionary hypernetwork is utilized for handwritten signature learning and verification. Finally,in order to test the efficiency of the proposed method,a signature dataset with 40 genuine signatures and 20 forged signatures of 20 writers is employed. The experimental results show that the false rejection rate (FRR),the false acceptance rate (FAR),and the verification accuracy (VA) of the proposed method are 4.75%,3.75%,and 95.75%,respectively. Moreover,the proposed model achieves higher classification accuracy than other traditional pattern recognition method.
作者
王进
谢水宁
颉小凤
LEE chongho
陈乔松
邓欣
WANG Jin1,XIE Shuining1,XIE Xiaofeng1,LEE Chongho2,CHEN Qiaosong1,DENG Xin1(Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,P.R.China;2.Department of Information and Communucation Engineering,Inha University,Incheon 402-751,Kore)
出处
《重庆邮电大学学报(自然科学版)》
CSCD
北大核心
2018年第3期399-407,共9页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
重庆市基础与前沿研究计划项目(cstc2014jcyjA40001,cstc2014jcyjA40022)
重庆教委科学技术研究项目(自然科学类)(KJ1400436)
重庆市研究生科研创新项目(CYS14150)
关键词
签名认证
笔迹特征集
向量化
演化超网络
signature verification
handwritten features
vectoring
evolutionary hypernetwork