Maergner, Paul
Pondenkandath, Vinaychandran
Alberti, Michele
Liwicki, Marcus
Riesen, Kaspar
Ingold, Rolf
Fischer, Andreas
Offline signature verification by combining graph edit distance and triplet networks
Proceedings of Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, 17-19 August 2018
978-3-319-97784-3
10.1007/978-3-319-97785-0_45
11 p.
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.
offline signature verification; graph edit distance; metric learning; deep convolutional neural network; triplet network; IngĂ©nierie;
17-19 August 2018
Beijing, China
2018-08
http://hesso.tind.io/record/3233/files/Riesen_2018_offline_signature_verification_graph_edit_distance_triplets_networks.pdf;