|We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test if this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring only a small amount of touch data.The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% for long-term authentication where the authentication test was one week after the enrollment phase.|
Example strokes from 8 users.
This dataset contains the raw touch data of 41 users interacting with Android smart phones plus a set of 30 extracted features for each touch stroke.Download:
Raw touch data, comma-separated.
The columns are 'phone ID','user ID', 'document ID', 'time[ms]', 'action', 'phone orientation', 'x-coordinate', 'y-coordinate', 'pressure', 'area covered', 'finger orientation'.
The column 'action' can take three values 0: touch down, 1: touch up, 2: move
Please find more detailed information in the readme.
features.mat (matlab format) and features.zip (comma separated):