Haichang Gao
Can background baroque music help to improve the memorability of graphical passwords?
Gao, Haichang; Chang, Xiuling; Ren, Zhongjie; Aickelin, Uwe; Wang, Liming
Authors
Xiuling Chang
Zhongjie Ren
Uwe Aickelin
Liming Wang
Contributors
Aur�lio Campilho
Editor
Mohamed Kamel
Editor
Abstract
Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, they still tend to follow predictable patterns that are easier for attackers to exploit, probably due to users’ memory limitations. Various literatures show that baroque music has positive effects on human learning and memorizing. To alleviate users’ memory burden, we investigate the novel idea of introducing baroque music to graphical password schemes (specifically DAS, PassPoints and Story) and conduct a laboratory study to see whether it is helpful. In a ten minutes short-term recall, we found that participants in all conditions had high recall success rates that were not statistically different from each other. After one week, the music group coped PassPoints passwords significantly better than the group without music. But there was no statistical difference between two groups in recalling DAS passwords or Story passwords. Further more, we found that the music group tended to set significantly more complicated PassPoints passwords but less complicated DAS passwords.
Citation
Gao, H., Chang, X., Ren, Z., Aickelin, U., & Wang, L. (2010, June). Can background baroque music help to improve the memorability of graphical passwords?. Presented at 7th international conference, ICIAR 2010, Povoa de Varzim, Portugal
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 7th international conference, ICIAR 2010 |
Start Date | Jun 21, 2010 |
End Date | Jun 23, 2010 |
Online Publication Date | Jun 21, 2010 |
Publication Date | Jun 21, 2010 |
Deposit Date | Feb 2, 2012 |
Publicly Available Date | Feb 2, 2012 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Pages | 378–387 |
Series Title | Lecture notes in computer science |
Series Number | 6112 |
Series ISSN | 1611-3349 |
Book Title | Image analysis and recognition |
ISBN | 9783642137747 |
DOI | https://doi.org/10.1007/978-3-642-13775-4_38 |
Public URL | https://nottingham-repository.worktribe.com/output/1012600 |
Publisher URL | https://link.springer.com/chapter/10.1007/978-3-642-13775-4_38 |
Files
gao2010.pdf
(254 Kb)
PDF
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.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