Abstract
In this paper, we present a novel approach to the verification of users through their own handwritten static signatures using the extreme learning machine (ELM) methodology. Our work uses the features extracted from the last fully connected layer of a deep learning pre-trained model to train our classifier. The final model classifies independent users by ranking them in a top list. In the proposed implementation, the training set can be extended easily to new users without the need for training the model every time from scratch. We have tested the state of the art deep neural networks for signature recognition on the largest available dataset and we have obtained an accuracy on average in the top 10 of more than 90%.
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The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
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Espinosa-Leal, L., Akusok, A., Lendasse, A., Björk, KM. (2021). Extreme Learning Machines for Signature Verification. In: Cao, J., Vong, C.M., Miche, Y., Lendasse, A. (eds) Proceedings of ELM2019. ELM 2019. Proceedings in Adaptation, Learning and Optimization, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-58989-9_4
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