基于多特征的在线签名认证方法

提出一种多特征的在线签名认证方法.该方法综合利用全局特征、笔段特征和签名的力序列和字形序列.采用一种F_Tablet手写板采集签名数据，该手写板可以记录签名时的字形序列和三维力序列.首先提取签名的全局特征，并定义特征重要性函数对特征进行选择，选取有利于正确区分真伪签名的个性全局特征，用基于概率的方法训练签名.接着将签名分段，提取每一笔段的笔段特征，建立基于笔段特征的隐马尔可夫模型.然后用动态时间规整的方法匹配签名的力信息和字形信息序列.最后综合利用多种特征来验证待测签名.该方法的等错误率为1.5%.

Multi-Feature Based Online Signature Verification

A multi-feature based online signature verification algorithm is presented that synthesizes global features, segment features, force series and shape series. A novel digital tablet called F_Tablet is used to capture both the shape series and the three-dimensional force series. Firstly, global features are extracted from the signature and weight function of features is defined to select the personalized global features and separate the genuine signatures from the fake ones. A probability method is used based on global features. Then, the signature is segmented and the segment features are extracted. A hidden Markov model is established based on segment features. The force series and shape series are matched with dynamic time warping. Finally, the multi-feature is synthesized to verify the test signatures and the proposed algorithm achieves equal error rate of 1.5%.