基于曲线分段相似匹配的在线签名认证

Online signature verification based on curve segment similarity matching

针对在线签名认证过程中出现的误匹配问题和曲线的缩放、旋转、位移以及采样不均匀导致的匹配距离过大的问题,提出一种基于曲线分段相似匹配的方法。在进行在线签名认证时,首先对两签名曲线进行分段粗匹配,主要应用了一种基于窗口累计差异矩阵的动态规划算法得到匹配关系。然后,对匹配对计算相似距离和加权累加和,主要方法是对曲线段进行拟合,在一定范围内进行相似变换,对其重采样并计算匹配对的欧氏距离。最后,取测试签名和所有模板签名的相似距离的平均值作为认证距离,将其与训练的阈值进行比较,从而判定真伪。在公开数据库SUSIG的Visual数据集和Blind数据集对该方法进行了测试,使用个性化阈值时分别可以得到3.56%和2.44%的等误率。所提方法在Blind数据集上的等误率比传统的动态时间规划(DTW)方法降低了约14.4%。实验结果表明,对熟练伪造签名和随机伪造签名的认证效果具有一定的优势。

Aiming at the problems of mismatching and too large matching distance because of curves scaling,shifting,rotation and non-uniform sampling in the process of online signature verification,a curve segment similarity matching method was proposed.In the progress of online signature verification,two curves were partitioned into segments and matched coarsely at first.A dynamic programming algorithm based on cumulative difference matrix of windows was introduced to get the matching relationship.Then,the similarity distance for each matching pair and weighted sum of all the matching pairs were calculated,and the calculating method is to fit each curve of matching pairs,carry out the similarity transformation within a certain range,and resample the curves to get the Euclidean distance.Finally,the average of the similarity distance between test signature and all template signatures was used as the authentication distance,which was compared with the training threshold to judge the authenticity.The method was validated on the open databases SUSIG Visual and SUSIG Blind respectively with 3.56%and 2.44%Equal Error Rate(EER)when using personalized threshold,and the EER was reduced by about 14.4%on Blind data set compared with the traditional Dynamic Time Wraping(DTW)method.The experimental results show that the proposed method has certain advantages in skilled forgery signature and random forgery signature verification.