Revealing Reliable Signatures by Learning Top-Rank Pairs

Xiaotong Ji, Yan Zheng, Daiki Suehiro, Seiichi Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instruments, ensuring the absolute reliability of signatures is of top priority. In this work, we proposed a new method to learn “top-rank pairs” for writer-independent offline signature verification tasks. By this scheme, it is possible to maximize the number of absolutely reliable signatures. More precisely, our method to learn top-rank pairs aims at pushing positive samples beyond negative samples, after pairing each of them with a genuine reference signature. In the experiment, BHSig-B and BHSig-H datasets are used for evaluation, on which the proposed model achieves overwhelming better pos@top (the ratio of absolute top positive samples to all of the positive samples) while showing encouraging performance on both Area Under the Curve (AUC) and accuracy.

Original languageEnglish
Title of host publicationDocument Analysis Systems - 15th IAPR International Workshop, DAS 2022, Proceedings
EditorsSeiichi Uchida, Elisa Barney, Véronique Eglin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages323-337
Number of pages15
ISBN (Print)9783031065545
DOIs
Publication statusPublished - 2022
Event15th IAPR International Workshop on Document Analysis Systems, DAS 2022 - La Rochelle, France
Duration: May 22 2022May 25 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13237 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th IAPR International Workshop on Document Analysis Systems, DAS 2022
Country/TerritoryFrance
CityLa Rochelle
Period5/22/225/25/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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