Abstract
In this paper, we present a novel methodology for the classification of static handwritten signatures. We extract features from each user’s high-pressure points by filtering the skeletonized-binarized digital signatures at seven different thresholds. We compare the sequences obtained using the Dynamic Time Warping (DTW) algorithm with five metrics for the x and y axes. The resulting distances are used as features in a binary dataset of genuine-genuine and genuine-forgery signatures with fourteen features. We train and compare three classifiers: Random Forest (RF), extreme gradient boosting (XGBoost), and Extreme Learning Machines (ELM) on the processed data from the MCYT-75 dataset [12]. Our results show that all models perform with similar accuracy, where the Euclidean and Manhattan metrics perform the best.
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The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
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Acosta-Velasquez, R., Espinosa-Leal, L., Garcia-Perez, A., Björk, KM. (2023). Verification of Static Signatures using Dynamic Time Warping on Features from High Pressure Points. In: Björk, KM. (eds) Proceedings of ELM 2021. ELM 2021. Proceedings in Adaptation, Learning and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-031-21678-7_10
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DOI: https://doi.org/10.1007/978-3-031-21678-7_10
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