FF Li
Sound-based multimodal person identification from signature and voice
Li, FF
Authors
Abstract
Person identification as a security means has a variety of important applications. Many techniques and automated systems have been developed over the past few decades; each has its own advantages and limitations. There are often trade-offs amongst reliability, the ease of use, ethical/human rights issues, and acceptability in a particular application. Multimodal identification and authentication can, to some extent, alleviate the dilemmas and improve the overall performance. This paper proposes a new method of the combined use of signatures and utterances of pronounced names to identify or authenticate persons. Unlike typical signature verification methods, the dynamic features of signatures are captured as sound in this paper. The multimodal approach shows increased reliability, providing a relatively simple and potentially useful method for person identification and authentication.
Citation
Li, F. (2010). Sound-based multimodal person identification from signature and voice. In Internet Monitoring and Protection (ICIMP), 2010 Fifth International Conference on 9-15 May 2010 (84-88). IEEE. https://doi.org/10.1109/ICIMP.2010.18
Publication Date | Jan 1, 2010 |
---|---|
Deposit Date | May 12, 2016 |
Pages | 84-88 |
Book Title | Internet Monitoring and Protection (ICIMP), 2010 Fifth International Conference on 9-15 May 2010 |
ISBN | 9781424467266 |
DOI | https://doi.org/10.1109/ICIMP.2010.18 |
Publisher URL | http://dx.doi.org/10.1109/ICIMP.2010.18 |
Related Public URLs | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5476374 |
Additional Information | Event Type : Conference |
You might also like
Microphone wind noise reduction using singular spectrum analysis techniques
(2017)
Presentation / Conference
Mitigating wind noise in outdoor microphone signals using a singular spectral subspace method
(2017)
Presentation / Conference
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search