  This paper focuses on how touch interactions on smartphones can provide a
continuous user authentication service through behaviour captured by a
touchscreen. While efforts are made to advance touch-based behavioural
authentication, researchers often focus on gathering data, tuning classifiers,
and enhancing performance by evaluating touch interactions in a sequence rather
than independently. However, such systems only work by providing data
representing distinct behavioural traits. The typical approach separates
behaviour into touch directions and creates multiple user profiles. This work
presents an omnidirectional approach which outperforms the traditional method
independent of the touch direction - depending on optimal behavioural features
and a balanced training set. Thus, we evaluate five behavioural feature sets
using the conventional approach against our direction-agnostic method while
testing several classifiers, including an Extra-Tree and Gradient Boosting
Classifier, which is often overlooked. Results show that in comparison with the
traditional, an Extra-Trees classifier and the proposed approach are superior
when combining strokes. However, the performance depends on the applied feature
set. We find that the TouchAlytics feature set outperforms others when using
our approach when combining three or more strokes. Finally, we highlight the
importance of reporting the mean area under the curve and equal error rate for
single-stroke performance and varying the sequence of strokes separately.
