Extreme Learning Machines for Signature Verification

Leonardo Espinosa-Leal; Anton Akusok; Amaury Lendasse; Kaj-Mikael Björk

doi:10.1007/978-3-030-58989-9_4

Extreme Learning Machines for Signature Verification

Leonardo Espinosa-Leal^*
, Anton Akusok
, 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

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%.

Original language	English
Title of host publication	Proceedings of ELM2019
Editors	Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Place of Publication	Cham
Publisher	Springer
Pages	31-40
ISBN (Electronic)	978-3-030-58989-9
ISBN (Print)	978-3-030-58988-2, 978-3-030-59049-9
DOIs	https://doi.org/10.1007/978-3-030-58989-9_4
Publication status	Published - 12 Sept 2020
MoE publication type	A4 Article in a conference publication
Event	2019 International Conference on Extreme Learning Machine (ELM 2019) - Yangzhou, China Duration: 14 Dec 2019 → 16 Dec 2019

Publication series

Series	Proceedings in Adaptation, Learning and Optimization
Volume	14
ISSN	2363-6084

Conference

Conference	2019 International Conference on Extreme Learning Machine (ELM 2019)
Country/Territory	China
City	Yangzhou
Period	14/12/19 → 16/12/19

Access to Document

10.1007/978-3-030-58989-9_4

Cite this

@inproceedings{907b6bfc8d3f409e9c88bb4db22a6b3f,

title = "Extreme Learning Machines for Signature Verification",

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\%.",

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

year = "2020",

month = sep,

day = "12",

doi = "10.1007/978-3-030-58989-9\_4",

language = "English",

isbn = "978-3-030-58988-2",

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

publisher = "Springer",

pages = "31--40",

editor = "Jiuwen Cao and Vong, \{Chi Man\} and Yoan Miche and Amaury Lendasse",

booktitle = "Proceedings of ELM2019",

address = "Germany",

note = "2019 International Conference on Extreme Learning Machine (ELM 2019) ; Conference date: 14-12-2019 Through 16-12-2019",

}

Espinosa-Leal, L, Akusok, A, Lendasse, A & Björk, K-M 2020, Extreme Learning Machines for Signature Verification. in J Cao, CM Vong, Y Miche & A Lendasse (eds), Proceedings of ELM2019. Springer, Cham, Proceedings in Adaptation, Learning and Optimization, vol. 14, pp. 31-40, 2019 International Conference on Extreme Learning Machine (ELM 2019), Yangzhou, China, 14/12/19. https://doi.org/10.1007/978-3-030-58989-9_4

Extreme Learning Machines for Signature Verification. / Espinosa-Leal, Leonardo; Akusok, Anton; Lendasse, Amaury et al.
Proceedings of ELM2019. ed. / Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lendasse. Cham: Springer, 2020. p. 31-40 (Proceedings in Adaptation, Learning and Optimization, Vol. 14).

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

TY - GEN

T1 - Extreme Learning Machines for Signature Verification

AU - Espinosa-Leal, Leonardo

AU - Akusok, Anton

AU - Lendasse, Amaury

AU - Björk, Kaj-Mikael

PY - 2020/9/12

Y1 - 2020/9/12

N2 - 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%.

AB - 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%.

U2 - 10.1007/978-3-030-58989-9_4

DO - 10.1007/978-3-030-58989-9_4

M3 - Conference article in proceedings

SN - 978-3-030-58988-2

SN - 978-3-030-59049-9

T3 - Proceedings in Adaptation, Learning and Optimization

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EP - 40

BT - Proceedings of ELM2019

A2 - Cao, Jiuwen

A2 - Vong, Chi Man

A2 - Miche, Yoan

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PB - Springer

CY - Cham

T2 - 2019 International Conference on Extreme Learning Machine (ELM 2019)

Y2 - 14 December 2019 through 16 December 2019

ER -

Extreme Learning Machines for Signature Verification

Abstract

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Cite this