Graphical Password-Based User Authentication with Free-Form Doodles
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
Institute of Electrical and Electronics Engineers Inc.Date
2016-08Citation
10.1109/THMS.2015.2504101
IEEE Transactions on Human-Machine Systems 46.4 (2016): 607-614
ISSN
2168-2291 (print); 2168-2305 (online)DOI
10.1109/THMS.2015.2504101Funded by
This work was supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Shield (TEC2012-34881) from Spanish MINECO, and BEAT (FP7-SEC-284989) from EU.Project
Comunidad de Madrid. S2009/TIC-1485/CONTEXTS; Gobierno de España. TEC2012-34881; info:eu-repo/grantAgreement/EC/FP7/284989Editor's Version
http://dx.doi.org/10.1109/THMS.2015.2504101Subjects
Dynamic time warping (DTW); Gaussian mixture models (GMMs); Gesture recognition; Graphical passwords; Mobile security; 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. M. Martinez-Diaz, J. Fierrez and J. Galbally, "Graphical Password-Based User Authentication With Free-Form Doodles," in IEEE Transactions on Human-Machine Systems, vol. 46, no. 4, pp. 607-614, Aug. 2016. doi: 10.1109/THMS.2015.2504101Rights
© 2015 IEEEAbstract
User authentication using simple gestures is now common in portable devices. In this work, authentication with free-form sketches is studied. Verification systems using dynamic time warping and Gaussian mixture models are proposed, based on dynamic signature verification approaches. The most discriminant features are studied using the sequential forward floating selection algorithm. The effects of the time lapse between capture sessions and the impact of the training set size are also studied. Development and validation experiments are performed using the DooDB database, which contains passwords from 100 users captured on a smartphone touchscreen. Equal error rates between 3% and 8% are obtained against random forgeries and between 21% and 22% against skilled forgeries. High variability between capture sessions increases the error rates.
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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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