Combining graph edit distance and triplet networks for offline signature verification

Maergner, Paul (DIVA, University of Fribourg, Fribourg, Switzerland) ; Pondenkandath, Vinaychandran (DIVA, University of Fribourg, Fribourg, Switzerland) ; Alberti, Michele (DIVA, University of Fribourg, Fribourg, Switzerland) ; Liwicki, Marcus (Lulea University of Technology, EISLAB Machine Learning, Lulea, Sweden) ; Riesen, Kaspar (Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland) ; Ingold, Rolf (DIVA, University of Fribourg, Fribourg, Switzerland) ; Fischer, Andreas (DIVA, University of Fribourg, Fribourg, Switzerland ; School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Date:
2019-07
Pagination:
8 p.
Published in:
Pattern Recognition Letters
Numeration (vol. no.):
2019, vol. 125, pp. 527-533
DOI:
ISSN:
0167-8655
Appears in Collection:

Note: The file is under embargo until: 2021-07-01


 Record created 2020-01-17, last modified 2020-01-21

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