Users’ individual differences in their mobile touch behaviour can help to continuously verify identity and protect personal data. However, little is known about the influence of GUI elements and hand postures on such touch biometrics. Thus, we present a metric to measure the amount of user-revealing information that can be extracted from touch targeting interactions and apply it in eight targeting tasks with over 150,000 touches from 24 users in two sessions. We compare touch-to-target offset patterns for four target types and two hand postures. Our analyses reveal that small, compactly shaped targets near screen edges yield the most descriptive touch targeting patterns. Moreover, our results show that thumb touches are more individual than index finger ones. We conclude that touch-based user identification systems should analyse GUI layouts and infer hand postures. We also describe a framework to estimate the usefulness of GUIs for touch biometrics.

Observing touch behaviour can yield important information about the user. This attractive data has been widely used in recent HCI research to personalise interfaces and to recognise individuals. Related work observed touches to tailor keyboards to the individual typist, to personalise fonts, to add an implicit layer to authentication (e.g. for unlock patterns), or to create new authentication methods based on touch biometrics. Targeting personalisation, privacy and security, these applications demonstrate the usefulness and importance of extracting and recognising user-specific information in mobile touch behaviour.

Related work has mostly targeted specific applications of touch biometrics, for example pattern unlock screens. Recently, features for touch biometrics have also been studied across a greater range of tasks. Improving our knowledge of what to observe (i.e. touch features) is important. On the other hand, to develop robust touch-based behavioural biometrics, we also need to investigate what influences the degree to which users will exhibit individual touch behaviour. We also lack a formal metric for such individuality.

Hence, to facilitate research and applications that use touch biometrics, we contribute: 1) an approach for measuring user-revealing information in targeting behaviour; 2) insights into influences of interface targets and hand postures on this individuality; and 3) an evaluation framework to estimate the expected amount of user-revealing information in touch interactions with given interfaces. Our insights inform applications of touch biometrics – focus on characteristic interactions and ignore those revealed as less individual.