{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/offline-signature-verification-by-combining","title":"Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks","arxiv_id":"1810.07491","date":"2018-10-17","proceeding":null,"authors":["Paul Maergner","Vinaychandran Pondenkandath","Michele Alberti","Marcus Liwicki","Kaspar Riesen","Rolf Ingold","Andreas Fischer"],"abstract":"Biometric authentication by means of handwritten signatures is a challenging\npattern recognition task, which aims to infer a writer model from only a\nhandful of genuine signatures. In order to make it more difficult for a forger\nto attack the verification system, a promising strategy is to combine different\nwriter models. In this work, we propose to complement a recent structural\napproach to offline signature verification based on graph edit distance with a\nstatistical approach based on metric learning with deep neural networks. On the\nMCYT and GPDS benchmark datasets, we demonstrate that combining the structural\nand statistical models leads to significant improvements in performance,\nprofiting from their complementary properties.","url_abs":"http://arxiv.org/abs/1810.07491v1","url_pdf":"http://arxiv.org/pdf/1810.07491v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"offline-signature-verification-by-combining","repo_url":"https://github.com/DIVA-DIA/SkeletonGraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":null,"task_name":"Triplet"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}