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Can background baroque music help to improve the memorability of graphical passwords?

Gao, Haichang; Chang, Xiuling; Ren, Zhongjie; Aickelin, Uwe; Wang, Liming

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Authors

Haichang Gao

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

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