On-line signature verification using local shape analysis

M. Zou, Jianjun Tong, Chang-ping Liu, Zhengliang Lou
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引用次数: 28

Abstract

This paper presents a novel approach to the on-line signature verification using local shape analysis. First, segment the input signature into several segments using HMM (hidden Markov model). Then, combine two adjacent segments to form a long segment and get its spectral and tremor information using FFT (fast Fourier transformation). At last, accept it or reject it based on the similarity between the spectral and its prototype. In addition, we proposed a novel initialization algorithm to avoid the local optimal of the HMM's re-estimation and a novel algorithm to avoid losing the important information at cusps in preprocessing. Combining the local shape analysis with the local time-based comparison, we get promising experimental results.
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基于局部形状分析的在线签名验证
提出了一种基于局部形状分析的在线签名验证方法。首先,使用隐马尔可夫模型(HMM)将输入签名分割成若干段。然后,将两个相邻的片段组合成一个长片段,利用快速傅里叶变换(FFT)得到其频谱和震颤信息。最后,根据光谱与其原型的相似度,对其进行接受或拒绝。此外,我们提出了一种新的初始化算法,以避免HMM重估计的局部最优,并提出了一种新的算法,以避免在预处理中丢失重要信息的尖端。将局部形状分析与基于局部时间的比较相结合,得到了令人满意的实验结果。
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