Signature biometrics is a widely used form of user authentication. As a behavioural biometric, samples have inherent inconsistencies which must be accounted for within an automated system. Performance deterioration of a tuned biometric software system may be caused by an interaction error with a biometric capture device, however, using conventional error metrics, system and user interaction errors are combined, thereby masking the contribution by each element. In this paper we explore the application of the Human-Biometric Sensor Interaction (HBSI) model to signature as an exemplar of a behavioural biometric. Using observational data collected from a range of subjects, our study shows that usability issues can be identified specific to individual capture device technologies. While most interactions are successful, a range of common interaction errors need to be mitigated by design to reduce overall error rates.
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