Online signature verification with new time series kernels for support vector machines

Authors:
Christian Gruber;Thiemo Gruber;Bernhard Sick
Affiliations:
Institute of Computer Architectures, University of Passau;Institute of Computer Architectures, University of Passau;Institute of Computer Architectures, University of Passau
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Abstract

In this paper, two new methods for online signature verification are proposed. The methods adopt the idea of the longest common subsequences (LCSS) algorithm to a kernel function for Support Vector Machines (SVM). The two kernels LCSS-global and LCSS-local offer the possibility to classify time series of different lengths with SVM. The similarity of two time series is determined very accurately since outliers are ignored. Consequently, LCSS-global and LCSS-local are more robust than algorithms based on dynamic time alignment such as Dynamic Time Warping (DTW). The new methods are compared to other kernel-based methods (DTW-kernel, Fisher-kernel, Gauss-kernel). Our experiments show that SVM with LCSS-local and LCSS-global authenticate persons very reliably.