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We study the feasibility of touch gesture behavioural biometrics for implicitauthentication of users on a smartglass (Google Glass) by proposing acontinuous authentication system using two classifiers: SVM with RBF kernel,and a new classifier based on Chebyshev's concentration inequality. Based ondata collected from 30 volunteers, we show that such authentication is feasibleboth in terms of classification accuracy and computational load onsmartglasses. We achieve a classification accuracy of up to 99% with only 75training samples using behavioural biometric data from four different types oftouch gestures. To show that our system can be generalized, we test itsperformance on touch data from smartphones and found the accuracy to be similarto smartglasses. Finally, our experiments on the permanence of gestures showthat the negative impact of changing user behaviour with time on classificationaccuracy can be best alleviated by periodically replacing older trainingsamples with new randomly chosen samples.

Gesture-based Continuous Authentication for Wearable Devices: the Google Glass Case