Implementing Biometric Security
Implementing Biometric Security
Biometric Systems: Technology, Design and Performance Evaluation
Biometric Systems: Technology, Design and Performance Evaluation
Enabling fast and effortless customisation in accelerometer based gesture interaction
Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia
Security Management for Mobile Devices by Face Recognition
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Authenticating mobile phone users using keystroke analysis
International Journal of Information Security
Handbook of Biometrics
Mobile Phones Security Using Biometrics
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 04
Score Based Biometric Template Selection and Update
FGCN '08 Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03
Improving hand-based verification through online finger template update based on fused confidences
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Online learning in biometrics: a case study in face classifier update
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Continuous Biometric Authentication: Can It Be More Practical?
HPCC '10 Proceedings of the 2010 IEEE 12th International Conference on High Performance Computing and Communications
Iris recognition in mobile phone based on adaptive gabor filter
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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This article focuses on the evaluation of a biometric technique based on the performance of an identifying gesture by holding a telephone with an embedded accelerometer in his/her hand. The acceleration signals obtained when users perform gestures are analyzed following a mathematical method based on global sequence alignment. In this article, eight different scores are proposed and evaluated in order to quantify the differences between gestures, obtaining an optimal EER result of 3.42% when analyzing a random set of 40 users of a database made up of 80 users with real attempts of falsification. Moreover, a temporal study of the technique is presented leeding to the need to update the template to adapt the manner in which users modify how they perform their identifying gesture over time. Six updating schemes have been assessed within a database of 22 users repeating their identifying gesture in 20 sessions over 4 months, concluding that the more often the template is updated the better and more stable performance the technique presents.