1. Graphical Password-Based User Authentication with Free-Form Doodles
- Author
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Marcos Martinez-Diaz, Javier Galbally, Julian Fierrez, UAM. Departamento de Tecnología Electrónica y de las Comunicaciones, and Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002)
- Subjects
Dynamic time warping ,Mobile security ,Computer Networks and Communications ,Computer science ,Speech recognition ,Feature extraction ,0211 other engineering and technologies ,Human Factors and Ergonomics ,02 engineering and technology ,law.invention ,Gesture recognition ,Graphical passwords ,Touchscreen ,Artificial Intelligence ,law ,0202 electrical engineering, electronic engineering, information engineering ,Pattern matching ,Gaussian mixture models (GMMs) ,Selection algorithm ,Password ,021110 strategic, defence & security studies ,Authentication ,Telecomunicaciones ,business.industry ,Pattern recognition ,Mixture model ,Dynamic time warping (DTW) ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Signal Processing ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
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.2015.2504101, 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., 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.
- Published
- 2016