Appearance-based gaze estimation models exhibit degraded accuracy when deployed on devices not present in the training data. For instance, iTracker, trained exclusively on the GazeCapture dataset collected from smartphones and tablets, shows increased error in the lower screen regions on laptops due to out-of-distribution downward gaze angles. We propose a lightweight fine-tuning approach that adapts a CoreML-format model using 13-point calibration data collected in approximately 30 seconds. In an evaluation with 11 participants, we compared four conditions: baseline, homography transformation, cross-user fine-tuning, and user-specific fine-tuning. User-specific fine-tuning reduced the mean estimation error from 6.17 cm to 2.91 cm (52.8% reduction), outperforming homography transformation (3.38 cm). These results demonstrate that fine-tuning only the final layer can effectively extend pre-trained gaze estimation models to unseen device configurations.

Designing realistic eye movements for animated avatars poses a challenge, as gaze behavior is predominantly unconscious. Accurately modulating those movements is crucial to avoid the Uncanny Valley. The human gaze exhibits different characteristics in conversations, depending on speaking or listening. Albeit these distinctions are known, data for synthesizing eye movement models suitable for avatars is scarce. This research introduces a novel dataset involving human gaze behavior during remote screen conversations. The data are collected from 19 participants, offering 4 hours of gaze data labeled as Speaking and Listening. Our data analysis substantiates prior knowledge of gaze behavior while providing new insights through higher precision. Furthermore, we demonstrate the dataset’s suitability for machine learning algorithms by training a classifier, achieving 88.1% binary classification accuracy.

The popularity of storing, distributing and reading comic books electronically has made the task of comics analysis an interesting research problem. Different work have been carried out aiming at understanding their layout structure and the graphic content. However the results are still far from universally applicable, largely due to the huge variety in expression styles and page arrangement, especially in manga (Japanese comics). In this paper, we propose a comic image analysis approach using eye-tracking data recorded during manga reading sessions. As humans are extremely capable of interpreting the structured drawing content, and show different reading behaviors based on the nature of the content, their eye movements follow distinguishable patterns over text or graphic regions. Therefore, eye gaze data can add rich information to the understanding of the manga content. Experimental results show that the fixations and saccades indeed form consistent patterns among readers, and can be used for manga textual and graphical analysis.

Reading is a very common learning activity, a lot of people perform it everyday even while standing in the subway or waiting in the doctors office. However, we know little about our everyday reading habits, quantifying them enables us to get more insights about better language skills, more effective learning and ultimately critical thinking. This paper presents a first contribution towards establishing a reading log, tracking how much reading you are doing at what time. We present an approach capable of estimating the words read by a user, evaluate it in an user independent approach over 3 experiments with 24 users over 5 different devices (e-ink reader, smartphone, tablet, paper, computer screen). We achieve an error rate as low as 5% (using a medical electrooculography system) or 15% (based on eye movements captured by optical eye tracking) over a total of 30 hours of recording. Our method works for both an optical eye tracking and an Electrooculography system. We provide first indications that the method works also on soon commercially available smart glasses.

The document analysis community spends substantial resources towards computer recognition of any type of text (e.g. characters, handwriting, document structure etc.). In this paper, we introduce a new paradigm focusing on recognizing the activities and habits of users while they are reading. We describe the differences to the traditional approaches of document analysis. We present initial work towards recognizing reading activities. We report our initial findings using a commercial, dry electrode Electroencephalography (EEG) system. We show the feasibility to distinguish reading tasks for 3 different document genres with one user and near perfect accuracy. Distinguishing reading tasks for 3 different document types we achieve 97 % with user specific training. We present evidence that reading and non-reading related activities can be separated over 3 users using 6 classes, perfectly separating reading from non-reading. A simple EEG system seems promising for distinguishing the reading of different document genres.