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 proceedingConference contributionScientificpeer-review


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
Number of pages10
Place of PublicationCham
Publication date2021
ISBN (Print) 978-3-030-58988-2
ISBN (Electronic)978-3-030-58989-9
Publication statusPublished - 2021
MoE publication typeA4 Article in conference proceedings

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


  • 113 Computer and information sciences


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

Cite this