Online Signature Verification with New Time Series Kernels for Support Vector Machines
Online Signature Verification with New Time Series Kernels for Support Vector Machines
复制标题
使用支持向量机的新时间序列内核进行在线签名验证
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
B. Sick
中科院分区：
文献类型：
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作者：
C. Gruber;Thiemo Gruber;B. Sick
本文提出了两种新的在线签名验证方法。该方法将最长公共子序列（LCSS）算法的思想转化为支持向量机的核函数。LCSS-global和LCSS-local两个核为支持向量机对不同长度的时间序列进行分类提供了可能。由于忽略了异常值，因此可以非常准确地确定两个时间序列的相似性。因此，LCSS-global和LCSS-local比基于动态时间对齐（如动态时间翘曲（DTW））的算法更健壮。将新方法与其他基于核的方法（DTW-kernel、Fisher-kernel、Gauss-kernel）进行了比较。实验表明，LCSS-local和LCSS-global支持向量机的身份验证非常可靠。
In this paper, two new methods for online signature verification are proposed. The methods adopt the idea of the longest common subsequences (LCSS) algorithm to a kernel function for Support Vector Machines (SVM). The two kernels LCSS-global and LCSS-local offer the possibility to classify time series of different lengths with SVM. The similarity of two time series is determined very accurately since outliers are ignored. Consequently, LCSS-global and LCSS-local are more robust than algorithms based on dynamic time alignment such as Dynamic Time Warping (DTW). The new methods are compared to other kernel-based methods (DTW-kernel, Fisher-kernel, Gauss-kernel). Our experiments show that SVM with LCSS-local and LCSS-global authenticate persons very reliably.