Abstract
Signature verification has a crucial role in individual authentication process, and forged signatures can cause vast damages in all the fields. An important aspect for signature verification is that there are millions of credit card transactions providing signatures; therefore, speedy checks are required. We propose using extreme learning machines (ELM) to help with the task. We demonstrate the effectiveness and efficiency of using ELM detecting forged signatures. The proposed method reports 0.27% equal error rate, compared to recent 0.88% reported results. Linear neuron type outperforms other neuron types in this task, as it converges the fastest regardless of feature extractors used. However, the best performance was still reached by using RELU with SigNet as feature extractor.
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The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
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Li, Z., Espinosa-Leal, L., Lendasse, A., Björk, KM. (2023). Extreme Learning Machines for Offline Forged Signature Identification. 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_3
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DOI: https://doi.org/10.1007/978-3-031-21678-7_3
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