Massive Offline Signature Forgery Detection with Extreme Learning Machines

Leonardo Espinosa-Leal; Zhen Li; Renjie Hu; Kaj-Mikael Björk

doi:10.1007/978-3-031-55056-0_2

Massive Offline Signature Forgery Detection with Extreme Learning Machines

Leonardo Espinosa-Leal^*, Zhen Li, Renjie Hu, 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

In this work, we present the results of different machine learning models for detecting offline signature forgeries trained from the features obtained from a massive dataset of ten thousand users. Features for training are obtained from the last layer of two different convolutional neural networks: Inception21k and Signet. Optimisation of the number of neurons and activation functions of Extreme Learning Machine (ELM) models are obtained using Equal Error Rate (EER) as a metric. Our results align with the recent results of other machine learning models. Furthermore, we found that a general-purpose network: Inception21k performs better for the writer-independent models created.

Original language	English
Title of host publication	Proceedings of ELM 2022
Subtitle of host publication	Theory, Algorithms and Applications
Editors	Kaj-Mikael Björk
Place of Publication	Cham
Publisher	Springer
Pages	9-15
ISBN (Electronic)	978-3-031-55056-0
ISBN (Print)	978-3-031-55055-3, 978-3-031-55058-4
DOIs	https://doi.org/10.1007/978-3-031-55056-0_2
Publication status	Published - 2024
MoE publication type	A4 Article in a conference publication
Event	12th International Conference on Extreme Learning Machines (ELM 2022) - Helsinki, Finland Duration: 8 Dec 2022 → 9 Dec 2022

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	18
ISSN	2363-6084

Conference

Conference	12th International Conference on Extreme Learning Machines (ELM 2022)
Country/Territory	Finland
City	Helsinki
Period	8/12/22 → 9/12/22

Access to Document

10.1007/978-3-031-55056-0_2

Cite this

@inproceedings{e94c01dec2be4248bdbe1520a8ca6e4f,

title = "Massive Offline Signature Forgery Detection with Extreme Learning Machines",

abstract = "In this work, we present the results of different machine learning models for detecting offline signature forgeries trained from the features obtained from a massive dataset of ten thousand users. Features for training are obtained from the last layer of two different convolutional neural networks: Inception21k and Signet. Optimisation of the number of neurons and activation functions of Extreme Learning Machine (ELM) models are obtained using Equal Error Rate (EER) as a metric. Our results align with the recent results of other machine learning models. Furthermore, we found that a general-purpose network: Inception21k performs better for the writer-independent models created.",

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

year = "2024",

doi = "10.1007/978-3-031-55056-0\_2",

language = "English",

isbn = "978-3-031-55055-3",

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

publisher = "Springer",

pages = "9--15",

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

booktitle = "Proceedings of ELM 2022",

address = "Germany",

note = "12th International Conference on Extreme Learning Machines (ELM 2022) ; Conference date: 08-12-2022 Through 09-12-2022",

}

Espinosa-Leal, L, Li, Z, Hu, R & Björk, K-M 2024, Massive Offline Signature Forgery Detection with Extreme Learning Machines. in K-M Björk (ed.), Proceedings of ELM 2022: Theory, Algorithms and Applications. Springer, Cham, Proceedings in Adaptation, Learning and Optimization, vol. 18, pp. 9-15, 12th International Conference on Extreme Learning Machines (ELM 2022), Helsinki, Finland, 8/12/22. https://doi.org/10.1007/978-3-031-55056-0_2

Massive Offline Signature Forgery Detection with Extreme Learning Machines. / Espinosa-Leal, Leonardo; Li, Zhen; Hu, Renjie et al.
Proceedings of ELM 2022: Theory, Algorithms and Applications. ed. / Kaj-Mikael Björk. Cham: Springer, 2024. p. 9-15 (Proceedings in Adaptation, Learning and Optimization, Vol. 18).

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

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AB - In this work, we present the results of different machine learning models for detecting offline signature forgeries trained from the features obtained from a massive dataset of ten thousand users. Features for training are obtained from the last layer of two different convolutional neural networks: Inception21k and Signet. Optimisation of the number of neurons and activation functions of Extreme Learning Machine (ELM) models are obtained using Equal Error Rate (EER) as a metric. Our results align with the recent results of other machine learning models. Furthermore, we found that a general-purpose network: Inception21k performs better for the writer-independent models created.

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