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.
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Keywords
- Support Vector Machine
- Kernel Function
- Gaussian Mixture Model
- Dynamic Time Warping
- Multivariate Time Series
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Gruber, C., Gruber, T., Sick, B. (2005). Online Signature Verification with New Time Series Kernels for Support Vector Machines. In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics. ICB 2006. Lecture Notes in Computer Science, vol 3832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11608288_67
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DOI: https://doi.org/10.1007/11608288_67
Publisher Name: Springer, Berlin, Heidelberg
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