Journal
PATTERN RECOGNITION LETTERS
Volume 125, Issue -, Pages 527-533Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2019.06.024
Keywords
Offline signature verification; Graph edit distance; Metric learning; Deep convolutional neural network; Triplet network
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Funding
- Swiss National Science Foundation [200021_162852]
- Swiss National Science Foundation (SNF) [200021_162852] Funding Source: Swiss National Science Foundation (SNF)
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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. (C) 2019 Elsevier B.V. All rights reserved.
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