LAUSR

The process of employing a dynamic signature verification system to verify the writer’s identity is known as online signature verification. It can be used as a security system to verify entrance applications and password substitutes, and as a forensic tool to support expert’s investigation, for example. This study proposes a novel online signature verification system based on a single-template strategy to improve performance in real-world scenarios. It uses discriminative mean signature template sets as well as fusion strategies of multiple local weighting and warping schemes for dynamic time warping (DTW). First, there is the creation of a set of user-specific mean signature templates for each feature using a recent time-series averaging method, i.e., Euclidean barycenter-based DTW barycenter averaging. Then, we acquire a local weighting estimate considering local stability sequences based on multiple and direct matching points between the mean signature templates and references for dependent and independent DTW. Moreover, we derive fusion strategies to calculate locally weighted DTW sets and concatenate them as a feature vector for each warping, followed by constructing a support vector machine (SVM) classifier, respectively. Finally, in the verification phase, we employ the single-template technique to obtain a discriminative fused score using SVMs between the mean template sets and a query sample. The suggested method’s efficiency is demonstrated by extensive experimental results acquired utilizing three public online signature datasets: SVC2004 Task1/Task2, and MCYT-100.