[1]马云鹏,李庆武,刘艳,等.基于图像特征融合识别的中文签名鉴伪方法[J].应用科技,2015,(06):10-14.[doi:10.11991/yykj.201504011]
 MA Yunpeng,LI Qingwu,LIU Yan,et al.Authenticity identification method for Chinese signature based on image feature fusion recognition[J].yykj,2015,(06):10-14.[doi:10.11991/yykj.201504011]
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基于图像特征融合识别的中文签名鉴伪方法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
期数:
2015年06期
页码:
10-14
栏目:
自动化技术
出版日期:
2015-12-05

文章信息/Info

Title:
Authenticity identification method for Chinese signature based on image feature fusion recognition
作者:
马云鹏1 李庆武12 刘艳1 曹美1
1. 河海大学 物联网工程学院, 江苏 常州 213022;
2. 常州市传感网与环境感知重点实验室, 江苏 常州 213022
Author(s):
MA Yunpeng1 LI Qingwu12 LIU Yan1 CAO Mei1
1 College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;
2 Changzhou Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou 213022, China
关键词:
签名鉴伪特征提取图像信息检测自适应特征融合夹角相似性
Keywords:
authenticity identification of signaturefeature extractionimage detectionself-adaptive features fusionvector angle similarity
分类号:
TP394.1
DOI:
10.11991/yykj.201504011
文献标志码:
A
摘要:
针对现存的签名鉴伪方法有效性低、鲁棒性差的问题,提出一种基于图像特征自适应融合识别的签名鉴伪方法。该方法首先对签名图像进行预处理并提取签名常规性特征和签名鲁棒性特征,然后对签名字符书写形态、单方向笔画重量分布、字符像素点分布、图像字符倾斜角度等图像信息进行检测,通过分析检测结果自适应地改变各特征的权重系数进行特征融合。利用待鉴伪签名融合特征与数据库融合特征的向量夹角相似性度量结果对鉴伪样本做出判断。实验结果表明该方法在签名鉴伪中具有良好的有效性、鲁棒性。
Abstract:
In view of the low validity and poor robustness of the existing authenticity identification method for signature, this paper proposes a method to indentify the authenticity of signature, based on self-adaptive fusion of image features. First, a signature image is pretreated, and normal features and robustness of the image are taken. Then, the writing form of characters, weight distribution of unidirectional strokes, pixel dot distribution of characters, inclination angle of image characters and other image information are inspected. By analyzing the inspection results, this method automatically changes the weight coefficient of each feature and fuses the features. Then this method judges the measurement results of the vector angle similarity between the fused features of signature to be identified and fused features of the data base. Result of the experiment shows this method has good validity and robustness in identifying the authenticity of signatures.

参考文献/References:

[1] 黄海龙, 王宏, 李微. 一种基于数学形态学的签名真伪鉴别方法[J]. 东北大学学报:自然科学版, 2011, 32(6): 854-858.
[2] 田伟, 乔谊正, 马志强. 基于DWT的二次特征提取脱机中文签名鉴定[J]. 山东大学学报:工学版, 2007, 37(3): 55-59.
[3] 朱皓悦, 耿国华, 周明全, 等. 基于LSVM分类鉴定器的脱机签名鉴定研究[J]. 计算机应用与软件, 2009, 26(7): 219-221.
[4] 梅园, 孙怀江, 夏德深. 一种基于改进后模板的图像快速细化算法[J]. 中国图形图像学报, 2006, 11(9): 1306-1310.
[5] 李莹. 汉字笔迹鉴别的算法研究[D]. 济南: 山东大学, 2007: 29-55.
[6] 高程程, 惠晓威. 基于灰度共生矩阵的纹理特征提取[J]. 计算机系统应用, 2010, 19(6): 195-198.
[7] 鄢煜尘. 基于信息融合的中文笔迹鉴别研究[D]. 武汉: 武汉大学, 2009: 29-60.
[8] 李昕, 丁晓青. 基于改进微结构特征的笔迹鉴别[J]. 清华大学学报(自然科学版), 2010, 50(4): 595- 600.
[9] ZAMORA-MARTINEZ F, FRINKEN V, ESPAÑA-BOQUERA S, et al. Neural network language models for off-line handwriting recognition[J]. Pattern Recognition, 2014, 47(4): 1642-1652.
[10] DREUW P, DOETSCH P, PLAHL C, et al. Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: a comparison for offline handwriting recognition[C]//Proceedings of the 2011 18th IEEE International Conference on Image Processing. Brussels, Belgium, 2011: 3541-3544.

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备注/Memo

备注/Memo:
收稿日期:2015-4-9;改回日期:。
基金项目:国家自然科学基金资助项目(41301448);江苏省产学研前瞻性联合研究资助项目(BY2014041);常州市科技支撑(社会发展)资助项目(CE20145038).
作者简介:马云鹏(1993-),男,硕士研究生;李庆武(1964-),男,教授,博士生导师.
通讯作者:李庆武,E-mail:liqw@hhuc.edu.cn.
更新日期/Last Update: 2016-01-07