Offline signature verification by combining graph edit distance and triplet networks

Maergner, Paul (University of Fribourg, Fribourg, Switzerland) ; Pondenkandath, Vinaychandran (University of Fribourg, Fribourg, Switzerland) ; Alberti, Michele (University of Fribourg, Fribourg, Switzerland) ; Liwicki, Marcus (University of Fribourg, Fribourg, Switzerland) ; Riesen, Kaspar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Ingold, Rolf (University of Fribourg, Fribourg, Switzerland) ; Fischer, Andreas (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; DIVA group, University of Fribourg, Fribourg, Switzerland)

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Subject(s):
Ingénierie
Publisher:
Beijing, China, 17-19 August 2018
Date:
2018-08
Beijing, China
17-19 August 2018
Pagination:
11 p.
Published in:
Proceedings of Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, 17-19 August 2018
DOI:
ISBN:
978-3-319-97784-3
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-02-26, last modified 2019-03-05

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