Abstract
This paper presents a novel feature extraction technique which is based on partial sum of second-order Taylor Series Expansion (TSE) for offline signature verification. Partial sum of TSE is calculated with finite number of terms within a small neighborhood of a point, yields approximation for the regular function. This essentially an effective mechanism to extract the localized structural features from signature. We propose kernel structures by incorporating the Sobel operators to compute the higher order derivatives of TSE. Support Vector Machine (SVM) classifier is employed for the signature verification. The outcome of experiments on standard signature datasets demonstrates the accuracy of the proposed approach. We performed a comparative analysis for our approach with some of the popular other approaches to exhibit the classification accuracy.
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Acknowledgements
We would like to acknowledge Bharathi R. K. for the support rendered in terms of providing a regional language dataset, namely, Mangalore University Kannada Off-line Signature (MUKOS) dataset.
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Shekar, B.H., Pilar, B., Sunil Kumar, D.S. (2019). Offline Signature Verification Based on Partial Sum of Second-Order Taylor Series Expansion. In: Nagabhushan, P., Guru, D., Shekar, B., Kumar, Y. (eds) Data Analytics and Learning. Lecture Notes in Networks and Systems, vol 43. Springer, Singapore. https://doi.org/10.1007/978-981-13-2514-4_30
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DOI: https://doi.org/10.1007/978-981-13-2514-4_30
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