Guest, Richard, Miguel-Hurtado, Oscar, Chatzisterkotis, Thomas (2017) A New Approach to Automatic Signature Complexity Assessment. In: 2016 IEEE International Carnahan Conference on Security Technology (ICCST). . IEEE ISBN 978-1-5090-1073-8. E-ISBN 978-1-5090-1072-1. (doi:10.1109/CCST.2016.7815678) (KAR id:58433)
PDF
Author's Accepted Manuscript
Language: English |
|
Download this file (PDF/6MB) |
|
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: https://doi.org/10.1109/CCST.2016.7815678 |
Abstract
Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric appbcations. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples.
Item Type: | Conference or workshop item (Paper) |
---|---|
DOI/Identification number: | 10.1109/CCST.2016.7815678 |
Subjects: | T Technology |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Tina Thompson |
Date Deposited: | 07 Nov 2016 10:16 UTC |
Last Modified: | 05 Nov 2024 10:49 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/58433 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):