基于段匹配差异观察值的HMM在线签名认证方法研究

提出一种用签名的分段差异值作为隐马尔可夫模型(HMM)观测值的在线签名认证应用方法。首先，采用双向后向合并DTW算法确定签名中关键点之间的对应关系。然后，采用经典DTW度量签名中各种细微的差异，用这些DTW差异值作为观测值训练HMM模型。将模型状态的意义定义为相似程度，将状态转移结构设定为全概率转移。在SVC2004签名数据库上，验证了该方法的有效性。

Study on Application of HMM to Online Signature Verification Based on Differences of Matched Segment

An approach of hidden markov model (HMM) to online signature verification is proposed, which uses difference values obtained by segmentation dynamic time wrapping (DTW) as observations of model. Firstly, the correspondences of the critical points in signatures are made by bidirectional backward-merging dynamic time wrapping algorithm. Then, the subtle differences are calculated by classical dynamic time wrapping algorithm. These differences are utilized to train the HMM. The meanings of models states are defined as degrees of similarity, and the HMM topology is ergodic. The validity of the proposed approach is verified on SVC2004 signatures database.