Template Matching Using Time-Series Averaging and DTW with Dependent Warping for Online Signature Verification

Abstract

Online signature verification has been widely applied in biometrics and forensics. Due to the recent demand on high-speed systems in this era of big data, to simultaneously improve its performance and calculation complexity, this study focuses on a singlelate strategy that uses dynamic time warping (DTW) with dependent warping for online signature verification, and attempts to construct a novel time-series averaging method called Euclidean barycenter-based DTW barycenter averaging (EB-DBA). Specifically, this study proposes a singlelate strategy using a mean template created by the EB-DBA to achieve higher performance at lower calculation complexity for online signature verification. The method's discriminative power is enhanced upon the exploration of two DTW warping types, where it is found that the DTW with dependent warping exhibits better performance. The popular MCYT-100 dataset is utilized in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower error rate and lower calculation complexity, for online signature verification.