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