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| 基于曲线相似的在线签名认证方法 | |
| 引用本文： | 邱益鸣,胡华成,郑建彬,陈庆虎. 基于曲线相似的在线签名认证方法[J]. 系统工程与电子技术, 2014, 36(5): 1016-1020. DOI: 10.3969/j.issn.1001-506X.2014.05.33 |
| 作者姓名： | 邱益鸣 胡华成 郑建彬 陈庆虎 |
| 作者单位： | 1. 武汉大学电子信息学院， 湖北 武汉 430072；2. 武汉理工大学信息工程学院， 湖北 武汉 430070 |
| 摘 要： | 提出一种新的曲线相似计算模型，用于在线签名身份认证。将在线签名轨迹数据看做平面曲线，从曲线相似性定义、相似变换及相似距离给出了曲线相似计算模型框架。依照参考签名曲线有效点数，对比较签名曲线进行重采样；签名曲线相似度量过程采用分段匹配方法，在每段比较曲线待匹配区间内，利用遗传算法搜索与参考曲线对应段的最优匹配，获得分段的相似距离，以此计算分段相似得分；将各段相似得分的平均值作为两条曲线的相似度量。选用SVC2004 Task1和SUSig Blind数据集对算法进行测试，等误率分别为10.92%和2.89%。 |
On-line handwriting signature verification based on curve similarity |
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| QIU Yi-ming,HU Hua-cheng,ZHENG Jian-bin,CHEN Qing-hu. On-line handwriting signature verification based on curve similarity[J]. System Engineering and Electronics, 2014, 36(5): 1016-1020. DOI: 10.3969/j.issn.1001-506X.2014.05.33 | |
| Authors: | QIU Yi-ming HU Hua-cheng ZHENG Jian-bin CHEN Qing-hu |
| Affiliation: | 1. School of Electronic Information, Wuhan University, Wuhan 430072, China;;2. School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China |
| Abstract: | A new curve similarity calculation model is presented for online signature verification. Conside ring online signature trajectory data as a plane curve, a curve similarity model is built based on the similarity definition, similarity transformation and curve similarity distance. The comparison signature curve is re sampled according to the effective point number of the reference one. In the calculation of curve similarity, the sectional matching is applied. In the range of each comparison curve interval to match, the sectional curve similarity distance is calculated by the genetic algorithm. The optimal sectional matching is found in the corresponding section of the reference curve. Then the sectional similarity distance is obtained and the estimate sectional similarity score is calculated. The mean of all similarity scores is defined as the curve similarity measure. The proposed algorithm is tested, and the equal error rates are 10.92% and 2.89% for SVC2004 Task1 and SUSIG Blind databases, respectively. |
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