基于细粒度动态特征的摹仿签名书写人识别
Identification of Imitative Signature Writers Based on Fine-grained Dynamic Features
齐明乐 1池长江 1李毅峰 1申思1
作者信息
- 1. 浙江警察学院,浙江 杭州,310053
- 折叠
摘要
电子签名笔迹逐渐取代传统笔迹,电子签名的真伪鉴别成为公安、司法鉴定领域的难题.于是提出了细粒度电子签名笔迹动态特征提取方法,利用K近邻、决策树、随机森林、支持向量机等监督学习方法综合分析摹仿电子签名的动、静态特征,建立摹仿电子签名笔迹书写人识别模型.实验结果显示,基于K近邻算法的书写人识别模型表现最好,正确率 0.917,精确率 0.906,召回率为0.871,AUC为 0.965.实验表明,笔迹动态特征能够显著提升摹仿签名书写人识别模型性能,增加样本类别数或者减低样本数量均会降低模型的识别能力.
Abstract
Electronic signature gradually replaces traditional handwriting,and the authentication of electronic sig-nature has become a difficult problem in the field of public security and judicial authentication.In this paper,a fine-grained electronic signature handwriting dynamic feature extraction method was proposed,and supervised learning methods including K-nearest neighbor,decision tree,random forest and support vector machine were used to compre-hensively analyze the dynamic features and static features of the imitation electronic signature.A classification model of the imitation electronic signature was established.The experiment results demonstrate that the writer recognition model based on K-nearest neighbor algorithm has the best performance,whose accuracy rate,precision rate,recall rate and the AUCis 0.917,0.906,0.871,and0.965,respectively.The experiment shows that the dynamic features of electronic handwriting can significantly improve the performance of writer classification model,the model's recognition ability declines when the categories of training sample or the number of training set decreasing.
关键词
电子签名笔迹/动态笔迹特征/机器学习/摹仿签名Key words
Electronic signature/Dynamic feature of handwriting/Machine learning/Imitating signature引用本文复制引用
出版年
2024
NETL
NSTL
万方数据