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Intra-class variation representation for on-line signature verification using wavelet and fractal analysis

机译:使用小波和分形分析进行在线签名验证的类内变化表示

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Signature is an important legal personal identification. Selecting a good feature representation is a significant step in designing a signature verification system. Single resolution function approach used in on-line signature verification faces the difficulty in identifying the intra-class variations of the features extracted. Such an approach might cause the acceptance of forged signatures that have similar patterns as the original and the rejection of genuine signatures that have high intra-class variations. This paper discusses the intra-class variation representation in on-line signature verification using wavelet and fractal analysis. With the achievement performance of an average improvement of 18% in genuine test verification rate and 7% in forged test verification rate compared to the single resolution function approach, it proves that the intra-class variations are important for on-line signature verification.
机译:签名是重要的合法个人身份证明。选择好的特征表示是设计签名验证系统的重要一步。在线签名验证中使用的单分辨率函数方法面临着识别所提取特征的类内变化的困难。这种方法可能会导致接受与原始样式具有相似模式的伪造签名,并拒绝具有高度内部差异的真实签名。本文讨论了使用小波和分形分析的在线签名验证中的类内变异表示。与单分辨率函数方法相比,真实测试验证率平均提高了18%,伪造测试验证率达到了7%,这证明了组内差异对于在线测试很重要签名验证。

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