We are increasingly required to prove our identity when using smartphones through explicit authentication processes such as passwords or physiological biometrics, e.g., authorising online banking transactions or unlocking smartphones. However, these methods are often annoying to input and do not guarantee that the genuine user remains the same. Thus, a modern verification process should differ from traditional authentication. In touch-based biometrics, a new approach must not verify what we draw but how we draw it. Our research proposes TouchEnc, a Deep Learning approach that outperforms conventional methods. Unlike Machine Learning methods, TouchEnc automates the feature extraction from touch gestures. TouchEnc achieves this by transforming and encoding touch behaviour into images, enabling continuous authentication through modern computer vision. Our approach has been tested on a popular and publicly available dataset to demonstrate its effectiveness. Results show that users can authenticate using TouchEnc with a single gesture containing users’ on-screen navigational behaviour, independent of drawing up, down, left, or right. TouchEnc achieves an 8.4% Equal Error Rate and a 96.7% Area Under the Curve using a single gesture. Furthermore, TouchEnc achieves up to 65% better Equal Error Rates when combining gestures compared to the related work.

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.