Extreme Learning Machines for Signature Verification

Leonardo Espinosa-Leal*, Anton Akusok, Amaury Lendasse, Kaj-Mikael Björk

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-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 languageEnglish
Title of host publicationProceedings of ELM2019
EditorsJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
PublisherSpringer
Publication date12.09.2020
Pages31-40
ISBN (Print) 978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
DOIs
Publication statusPublished - 12.09.2020
MoE publication typeA3 Book chapter

Publication series

NameProceedings of ELM2019
Volume14
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092

Fingerprint Dive into the research topics of 'Extreme Learning Machines for Signature Verification'. Together they form a unique fingerprint.

  • Cite this

    Espinosa-Leal, L., Akusok, A., Lendasse, A., & Björk, K-M. (2020). Extreme Learning Machines for Signature Verification. In J. Cao, C. M. Vong, Y. Miche, & A. Lendasse (Eds.), Proceedings of ELM2019 (pp. 31-40). (Proceedings of ELM2019; Vol. 14). Springer. https://doi.org/10.1007/978-3-030-58989-9_4