Name
Abstract | ||
|---|---|---|
Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/7107295 | WIRELESS COMMUNICATIONS & MOBILE COMPUTING |
Field | DocType | Volume |
Data mining,Authentication,Decorrelation,Computer science,Fingerprint,Security analysis,Mobile device,Biometrics,Information privacy,Nonlinear distortion,Distributed computing | Journal | 2018 |
ISSN | Citations | PageRank |
1530-8669 | 5 | 0.41 |
References | Authors | |
37 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wencheng Yang | 1 | 85 | 10.34 |
| Jiankun Hu | 2 | 1976 | 150.35 |
| Song Wang | 3 | 321 | 16.09 |
| Qianhong Wu | 4 | 87 | 11.95 |