| 基于小波包分析和SVM的在线手写签名鉴别 |
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| 引用本文： | 马海豹,刘漫丹,张岑.基于小波包分析和SVM的在线手写签名鉴别[J].医学教育探索,2007(4):541-545. |
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| 作者姓名： | 马海豹 刘漫丹 张岑 |
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| 作者单位： | 华东理工大学自动化研究所; |
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| 摘 要： | 针对在线手写签名难以提取有效特征的实际情况,提出用小波包分解和单支重构来构造能量特征向量的方法,直接利用各频段成分能量的变化来反映签名的动态特征。给出了衡量各特征识别能力的Fisher准则,并且基于该准则剔除了识别能力差的特征,优化了特征空间。用该方法构造的特征向量能突出反映签名的动态特征。然后采用SVM对签名进行识别。实验证明:采用本文方法识别的正确率高达99.38%,错误拒绝率FRR=0.25%,错误接受率FAR=1.0%,其性能令人满意。
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| 关 键 词： | 手写签名鉴别 小波包分析 特征提取 SVM Fisher准则 |
A Method of On line Handwritten Signature Verification Based on Wavelet Packet Analysis and SVM |
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| MA Hai-bao,LIU Man-dan,ZHANG cen.A Method of On line Handwritten Signature Verification Based on Wavelet Packet Analysis and SVM[J].Researches in Medical Education,2007(4):541-545. |
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| Authors: | MA Hai-bao LIU Man-dan ZHANG cen |
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| Institution: | Research Institute of Automation;East China University of Science and Technology;Shanghai 200237;China |
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| Abstract: | It is difficult to extract effective features of on line signature verification,so a new method of dynamic feature extraction is proposed based on decomposition and reconstruction of wavelet packet in this paper.The feature vectors that reflect the energy change of different frequency ranges are constructed by the method and the Fisher criterion was used to select the effective features. With this criterion,some features that contain little discriminating information are discarded in order to simplify featu... |
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| Keywords: | handwritten signature verification wavelet packet analysis feature extraction SVM Fisher criterion |
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