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Combining graph edit distance and triplet networks for offline signature verification
Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
Department of Informatics, DIVA Group, University of Fribourg, Fribourg, Switzerland.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
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2019 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 125, p. 527-533Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 125, p. 527-533
Keywords [en]
Offline signature verification, Graph edit distance, Metric learning, Deep convolutional neural network, Triplet network
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-75255DOI: 10.1016/j.patrec.2019.06.024ISI: 000482374500072Scopus ID: 2-s2.0-85067868377OAI: oai:DiVA.org:ltu-75255DiVA, id: diva2:1335964
Note

Validerad;2019;Nivå 2;2019-08-20 (johcin)

Available from: 2019-07-08 Created: 2019-07-08 Last updated: 2019-09-13Bibliographically approved

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Liwicki, Marcus

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Output format
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