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 how 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 interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intra-session authentication, 2%-3% for inter-session authentication and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multi-modal biometric authentication system.
While lots of reading happens on mobile devices, little research has been performed on how the reading-interaction actually takes place. Therefore we describe our findings on a study conducted with 18 users which were asked to read a number of texts while their touch and gaze data was being recorded. We found three reader types and identified their preferred alignment of text on the screen. Based on our findings we are able to computationally estimate the reading area with an approximate .81 precision and .89 recall. Our computed reading speed estimate has an average 10.9% wpm error in contrast to the measured speed, and combining both techniques we can pinpoint the reading location at a given time with an overall word error of 9.26 words, or about three lines of text on our device.
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