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 intrasession authentication, 2%–3% for intersession 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 multimodal 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.

An increasing amount of text is being read digitally. In this paper we explore how eye tracking devices can be used to aggregate reading data of many readers in order to provide authors and editors with objective and implicitly gathered quality feedback. We present a robust way to jointly evaluate the gaze data of multiple readers, with respect to various reading-related features. We conducted an experiment in which a group of high school students composed essays subsequently read and rated by a group of seven other students. Analyzing the recorded data, we find that the amount of regression targets, the reading-to-skimming ratio, reading speed and reading count are the most discriminative features to distinguish very comprehensible from barely comprehensible text passages. By employing machine learning techniques, we are able to classify the comprehensibility of text automatically with an overall accuracy of 62%.

In this paper we present an approach to build an eye-tracking based text cursor placement system. When triggered, the system employs a computer vision based analysis of the screen's content around the current gaze position to find the most likely designated gaze target. Eventually it synthesizes a mouse event at that position, allowing for a rapid text cursor repositioning even in applications which do not support eye tracking explicitly. For our system we compared three different computer vision methods in a simulation run and evaluated the best candidate in two double blinded user studies. We used a total of 19 participants to assess the system's objective and perceived end user speed up. We can demonstrate that in terms of reposition time the OCR based method is superior to the other tested methods, it also beats common keyboard-mouse interaction for some users. We conclude that while the tool was almost universally preferred subjectively over keyboard-mouse interaction, the highest speed can be achieved by using the right amount of eye tracking.

Distinguishing whether eye tracking data reflects reading or skimming already proved to be of high analytical value. But with a potentially more widespread usage of eye tracking systems at home, in the office or on the road the amount of environmental and experimental control tends to decrease. This in turn leads to an increase in eye tracking noise and inaccuracies which are difficult to address with current reading detection algorithms. In this paper we propose a method for constructing and training a classifier that is able to robustly distinguish reading from skimming patterns. It operates in real time, considering a window of saccades and computing features such as the average forward speed and angularity. The algorithm inherently deals with distorted eye tracking data and provides a robust, linear classification into the two classes read and skimmed. It facilitates reaction times of 750ms on average, is adjustable in its horizontal sensitivity and provides confidence values for its classification results; it is also straightforward to implement. Trained on a set of six users and evaluated on an independent test set of six different users it achieved a 86% classification accuracy and it outperformed two other methods.

Reading is one of the most frequent activities of knowledge workers. Eye tracking can provide information on what document parts users read, and how they were read. This article aims at generating implicit relevance feedback from eye movements that can be used for information retrieval personalization and further applications. We report the findings from two studies which examine the relation between several eye movement measures and user-perceived relevance of read text passages. The results show that the measures are generally noisy, but after personalizing them we find clear relations between the measures and relevance. In addition, the second study demonstrates the effect of using reading behavior as implicit relevance feedback for personalizing search. The results indicate that gaze-based feedback is very useful and can greatly improve the quality of Web search. The article concludes with an outlook introducing attentive documents keeping track of how users consume them. Based on eye movement feedback, we describe a number of possible applications to make working with documents more effective.