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• W3105131404 abstract "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." @default.
• W3105131404 created "2020-11-23" @default.
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• W3105131404 date "2018-01-01" @default.
• W3105131404 modified "2023-10-16" @default.
• W3105131404 title "Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks" @default.
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• W3105131404 doi "https://doi.org/10.1007/978-3-319-97785-0_45" @default.
• W3105131404 hasPublicationYear "2018" @default.
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