Extreme Learning Machines for Signature Verification

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

*Motsvarande författare för detta arbete

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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%.
OriginalspråkEngelska
Titel på gästpublikationProceedings of ELM2019
RedaktörerJiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Antal sidor10
UtgivningsortCham
FörlagSpringer
Utgivningsdatum2021
Sidor31-40
ISBN (tryckt) 978-3-030-58988-2
ISBN (elektroniskt)978-3-030-58989-9
DOI
StatusPublicerad - 2021
MoE-publikationstypA4 Artikel i en konferenspublikation

Publikationsserier

NamnProceedings in Adaptation, Learning and Optimization
Volym14
ISSN (tryckt)2363-6084
ISSN (elektroniskt)2363-6092

Nyckelord

  • 113 Data- och informationsvetenskap

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