Extreme Learning Machines for Offline Forged Signature Identification

Zhen Li; Leonardo Espinosa-Leal; Amaury Lendasse; Kaj-Mikael Björk

doi:10.1007/978-3-031-21678-7_3

Extreme Learning Machines for Offline Forged Signature Identification

Zhen Li^*
, Leonardo Espinosa-Leal
, Amaury Lendasse
, Kaj-Mikael Björk

^*Corresponding author for this work

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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.

Original language	English
Title of host publication	Proceedings of ELM 2021
Subtitle of host publication	Theory, Algorithms and Applications
Editors	Kaj-Mikael Björk
Place of Publication	Cham
Publisher	Springer
Pages	24-31
ISBN (Electronic)	978-3-031-21678-7
ISBN (Print)	978-3-031-21677-0, 978-3-031-21680-0
DOIs	https://doi.org/10.1007/978-3-031-21678-7_3
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	11th International Conference on Extreme Learning Machines (ELM2021) - On-line, Helsinki, Finland Duration: 15 Dec 2021 → 16 Dec 2021 Conference number: 11 https://risklab.fi/events/

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	16
ISSN	2363-6084

Conference

Conference	11th International Conference on Extreme Learning Machines (ELM2021)
Abbreviated title	ELM2021
Country/Territory	Finland
City	Helsinki
Period	15/12/21 → 16/12/21
Internet address	https://risklab.fi/events/

Access to Document

10.1007/978-3-031-21678-7_3

Cite this

@inproceedings{d121496ce3364a9c9f32210cfb9a8056,

title = "Extreme Learning Machines for Offline Forged Signature Identification",

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.",

author = "Zhen Li and Leonardo Espinosa-Leal and Amaury Lendasse and Kaj-Mikael Bj{\"o}rk",

year = "2023",

doi = "10.1007/978-3-031-21678-7\_3",

language = "English",

isbn = "978-3-031-21677-0",

series = "Proceedings in Adaptation, Learning and Optimization",

publisher = "Springer",

pages = "24--31",

editor = "Kaj-Mikael Bj{\"o}rk",

booktitle = "Proceedings of ELM 2021",

address = "Germany",

note = "11th International Conference on Extreme Learning Machines (ELM2021), ELM2021 ; Conference date: 15-12-2021 Through 16-12-2021",

url = "https://risklab.fi/events/",

}

Li, Z, Espinosa-Leal, L, Lendasse, A & Björk, K-M 2023, Extreme Learning Machines for Offline Forged Signature Identification. in K-M Björk (ed.), Proceedings of ELM 2021: Theory, Algorithms and Applications. Springer, Cham, Proceedings in Adaptation, Learning and Optimization, vol. 16, pp. 24-31, 11th International Conference on Extreme Learning Machines (ELM2021), Helsinki, Finland, 15/12/21. https://doi.org/10.1007/978-3-031-21678-7_3

Extreme Learning Machines for Offline Forged Signature Identification. / Li, Zhen; Espinosa-Leal, Leonardo; Lendasse, Amaury et al.
Proceedings of ELM 2021: Theory, Algorithms and Applications. ed. / Kaj-Mikael Björk. Cham: Springer, 2023. p. 24-31 (Proceedings in Adaptation, Learning and Optimization, Vol. 16).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Extreme Learning Machines for Offline Forged Signature Identification

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AU - Espinosa-Leal, Leonardo

AU - Lendasse, Amaury

AU - Björk, Kaj-Mikael

N1 - Conference code: 11

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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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DO - 10.1007/978-3-031-21678-7_3

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SN - 978-3-031-21680-0

T3 - Proceedings in Adaptation, Learning and Optimization

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