Benchmarking Touchscreen Biometrics for Mobile Authentication

We study user interaction with touchscreens based on swipe gestures for personal authentication. This approach has been analyzed only recently in the last few years in a series of disconnected and limited works. We summarize those recent efforts and then compare them to three new systems (based on support vector machine and Gaussian mixture model using selected features from the literature) exploiting independent processing of the swipes according to their orientation. For the analysis, four public databases consisting of touch data obtained from gestures sliding one finger on the screen are used. We first analyze the contents of the databases, observing various behavioral patterns, e.g., horizontal swipes are faster than vertical independently of the device orientation. We then explore an intra-session scenario, where users are enrolled and authenticated within the same day, and an inter-session one, where enrollment and test are performed on different days. The resulting benchmarks and processed data are made public, allowing the reproducibility of the key results obtained based on the provided score files and scripts. In addition to the remarkable performance, thanks to the proposed orientation-based conditional processing, the results show various new insights into the distinctiveness of swipe interaction, e.g., some gestures hold more user-discriminant information, data from landscape orientation is more stable, and horizontal gestures are more discriminative in general than vertical ones.