Mobile user authentication using statistical touch dynamics images

Behavioral biometrics have recently begun to gain attention for mobile user authentication. The feasibility of touch gestures as a novel modality for behavioral biometrics has been investigated. In this paper, we propose applying a statistical touch dynamics image (aka statistical feature model) trained from graphic touch gesture features to retain discriminative power for user authentication while significantly reducing computational time during online authentication. Systematic evaluation and comparisons with state-of-the-art methods have been performed on touch gesture data sets. Implemented as an Android App, the usability and effectiveness of the proposed method have also been evaluated.