User Dependent Template Update for Keystroke Dynamics Recognition

Regarding the fact that individuals have different interactions with biometric authentication systems, several techniques have been developed in the literature to model different users categories. Doddington Zoo is a concept of categorizing users behaviors into animal groups to reflect their characteristics with respect to biometric systems. This concept was developed for different biometric modalities including keystroke dynamics. The present study extends this biometric classification, by proposing a novel adaptive strategy based on the Doddinghton Zoo, for the recognition of the user's keystroke dynamics. The obtained results demonstrate competitive performances on significant keystroke dynamics datasets.

[1]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Adaptive approaches for keystroke dynamics , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[2]  Christophe Rosenberger,et al.  Hybrid template update system for unimodal biometric systems , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[3]  Julian Fiérrez,et al.  Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics , 2014, ECCV Workshops.

[4]  Josef Kittler,et al.  Model and Score Adaptation for Biometric Systems: Coping With Device Interoperability and Changing Acquisition Conditions , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Gian Luca Marcialis,et al.  Analysis of unsupervised template update in biometric recognition systems , 2014, Pattern Recognit. Lett..

[6]  Neil Yager,et al.  Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[7]  Robert Sabourin,et al.  Context-Sensitive Self-Updating for Adaptive Face Recognition , 2015 .

[8]  Md. Kamrul Hasan,et al.  Identifying emotion by keystroke dynamics and text pattern analysis , 2014, Behav. Inf. Technol..

[9]  Christophe Rosenberger,et al.  Towards a Secured Authentication Based on an Online Double Serial Adaptive Mechanism of Users' Keystroke Dynamics , 2018 .

[10]  Douglas A. Reynolds,et al.  SHEEP, GOATS, LAMBS and WOLVES A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation , 1998 .

[11]  Norman Shapiro,et al.  Authentication by Keystroke Timing: Some Preliminary Results , 1980 .

[12]  Christophe Rosenberger,et al.  Keystroke template update with adapted thresholds , 2016, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[13]  Christophe Rosenberger,et al.  Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[14]  Christophe Rosenberger,et al.  Adaptive Biometric Strategy using Doddington Zoo Classification of User’s Keystroke Dynamics , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[15]  Sungzoon Cho,et al.  Continual Retraining of Keystroke Dynamics Based Authenticator , 2007, ICB.

[16]  Regan L. Mandryk,et al.  Identifying emotional states using keystroke dynamics , 2011, CHI.

[17]  Gian Luca Marcialis,et al.  Template Update Methods in Adaptive Biometric Systems: A Critical Review , 2009, ICB.

[18]  Marlies Rybnicek,et al.  A roadmap to continuous biometric authentication on mobile devices , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[19]  Fabio Roli,et al.  Critical analysis of adaptive biometric systems , 2012, IET Biom..

[20]  Sonia Garcia-Salicetti,et al.  On Hunting Animals of the Biometric Menagerie for Online Signature , 2016, PloS one.