  The field of behavioural biometrics stands as an appealing alternative to
more traditional biometric systems due to the ease of use from a user
perspective and potential robustness to presentation attacks. This paper
focuses its attention to a specific type of behavioural biometric utilising
swipe dynamics, also referred to as touch gestures. In touch gesture
authentication, a user swipes across the touchscreen of a mobile device to
perform an authentication attempt. A key characteristic of touch gesture
authentication and new behavioural biometrics in general is the lack of
available data to train and validate models. From a machine learning
perspective, this presents the classic curse of dimensionality problem and the
methodology presented here focuses on Bayesian unsupervised models as they are
well suited to such conditions. This paper presents results from a set of
experiments consisting of 38 sessions with labelled victim as well as blind and
over-the-shoulder presentation attacks. Three models are compared using this
dataset; two single-mode models: a shrunk covariance estimate and a Bayesian
Gaussian distribution, as well as a Bayesian non-parametric infinite mixture of
Gaussians, modelled as a Dirichlet Process. Equal error rates (EER) for the
three models are compared and attention is paid to how these vary across the
two single-mode models at differing numbers of enrolment samples.
