Automatic Signature Stability Analysis and Verification Using Local Features

Muhammad Imran Malik, Marcus Liwicki, Andreas Dengel, Seiichi Uchida, Volkmar Frinken

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

25 Citations (Scopus)

Abstract

The purpose of writing this paper is two-fold. First, it presents a novel signature stability analysis based on signature's local / part-based features. The Speeded Up Local features (SURF) are used for local analysis which give various clues about the potential areas from whom the features should be exclusively considered while performing signature verification. Second, based on the results of the local stability analysis we present a novel signature verification system and evaluate this system on the publicly available dataset of forensic signature verification competition, 4NSigComp2010, which contains genuine, forged, and disguised signatures. The proposed system achieved an EER of 15%, which is considerably very low when compared against all the participants of the said competition. Furthermore, we also compare the proposed system with some of the earlier reported systems on the said data. The proposed system also outperforms these systems.

Original languageEnglish
Title of host publicationProceedings - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages621-626
Number of pages6
ISBN (Electronic)9781479943340
DOIs
Publication statusPublished - Dec 9 2014
Event14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014 - Hersonissos, Crete Island, Greece
Duration: Sep 1 2014Sep 4 2014

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume2014-December
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Other

Other14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
CountryGreece
CityHersonissos, Crete Island
Period9/1/149/4/14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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