Time-series averaging and local stability-weighted dynamic time warping for online signature verification. (April 2021)

• Record Type: Journal Article
• Authors: Okawa, Manabu
• Is Part Of: Pattern recognition. Volume 112(2021)
• Publication Date: 2021-04
• Subjects: Biometrics -- Signature verification -- In-air signature -- Time-series analysis -- Dynamic time warping (DTW) -- Euclidean barycenter-based DTW barycenter averaging (EB-DBA) -- Local stability-weighted DTW (LS-DTW); Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107699
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a novel single-template matching method for online signature verification. The single-template strategy adopts a mean template set and LS-DTW. The mean template is created by a recent time-series averaging method, EB-DBA. The LS-DTW is calculated by incorporating the local stability when matching two signatures. The proposed method achieves higher performance than existing signature verification systems. Abstract: To meet the recent demands for automated security systems, this study proposes a novel single-template strategy that uses mean templates and local stability-weighted dynamic time warping (LS-DTW) to simultaneously improve the speed and accuracy of online signature verification. Specifically, we adopt a recent time-series averaging method, called Euclidean barycenter-based DTW barycenter averaging (EB-DBA), to obtain an effective mean template set for each feature while preserving intra-user variability among reference samples. We then estimate the local stability of the mean template set by using direct matching points that represent stable signature regions in the DTW warping paths between the mean template set and the references. Subsequently, we boost the discriminative power in the verification phase using the LS-DTW distance measure that incorporates the local stability sequence as the weights for the DTW cost function between the mean template set and a query signature. Finally, we use the public SVC2004 Task2/MCYT-100 online signature datasets and the recent 3DAirSig in-air signature dataset to conduct experiments, whose results confirm the effectiveness of the proposed method in both the random- and skilled-forgery scenarios.