Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
The doodb graphical password database: data analysis and benchmark results
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
IEEEDate
2013-09-25Citation
10.1109/ACCESS.2013.2281773
IEEE Access 1 (2013): 596-605
ISSN
2169-3536DOI
10.1109/ACCESS.2013.2281773Funded by
This work was supported by projects Contexts under Grant S2009/TIC-1485 from CAM, Bio-Challenge under Grant TEC2009-11186 and Bio-Shield under Grant TEC2012-34881 from Spanish MECD, and BEAT under Grant FP7-SEC-284989 from EUProject
Comunidad de Madrid. S2009/TIC-1485/CONTEXTS; info:eu-repo/grantAgreement/EC/FP7/102363Editor's Version
http://dx.doi.org/10.1109/ACCESS.2013.2281773Subjects
Graphical password; doodle verification; pseudo-signature; TelecomunicacionesNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Martinez-Diaz, M.; Fierrez, J. ; Galbally, J. "The DooDB Graphical Password Database: Data Analysis and Benchmark Results" IEEE Access, vol. 1 pp. 596 - 605, 2013Rights
© 2013 IEEEAbstract
We present DooDB, a doodle database containing data from 100 users captured with a touch screen-enabled mobile device under realistic conditions following a systematic protocol. The database contains two corpora: 1) doodles and 2) pseudo-signatures, which are simplified finger-drawn versions of the handwritten signature. The dataset includes genuine samples and forgeries, produced under worst-case conditions, where attackers have visual access to the drawing process. Statistical and qualitative analyses of the data are presented, comparing doodles and pseudo-signatures to handwritten signatures. Time variability, learning curves, and discriminative power of different features are also studied. Verification performance against forgeries is analyzed using state-of-the-art algorithms and benchmark results are provided.
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Google Scholar:Martínez Díaz, Marcos
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Fiérrez Aguilar, Julián
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Galbally Herrero, Javier
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