Most eye tracking methods are light-based. As such they can suffer from ambient light changes when used outdoors. It has been suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera based sensors for eye tracking. We designed a bench top experimental setup to investigate the utility of ultrasound for eye tracking, and collected time of flight and amplitude data for a range of gaze angles of a model eye. We used this data as input for a machine learning model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 1.021 +- 0.189 degrees with an adjusted R2 score of 89.92 +- 4.9).

In this paper, we focus on the calibration possibilities of a deep learning based gaze estimation process applying transfer learning, comparing its performance when using a general dataset versus when using a gaze specific dataset in the pretrained model. Subject calibration has demonstrated to improve gaze accuracy in high performance eye trackers. Hence, we wonder about the potential of a deep learning gaze estimation model for subject calibration employing fine-tuning procedures. A pretrained Resnet-18 network, which has great performance in many computer vision tasks, is fine-tuned using user's specific data in a few shot adaptive gaze estimation approach. We study the impact of pretraining a model with a synthetic dataset, U2Eyes, before addressing the gaze estimation calibration in a real dataset, I2Head. The results of the work show that the success of the individual calibration largely depends on the balance between fine-tuning and the standard supervised learning procedures and that using a gaze specific dataset to pretrain the model improves the accuracy when few images are available for calibration. This paper shows that calibration is feasible in low resolution scenarios providing outstanding accuracies below 1.5 ° of error.

Recently, deep learning approaches have been proposed to detect eye movements such as fixations, saccades, and smooth pursuits from eye tracking data. These are end-to-end methods that have shown to surpass traditional ones, requiring no ad hoc parameters. In this work we propose the use of temporal convolutional networks (TCNs) for automated eye movement classification and investigate the influence of feature space, scale, and context window sizes on the classiffication results. We evaluated the performance of TCNs against a state-of-the-art 1DCNN-BLSTM model using GazeCom, a public available dataset. Our results show that TCNs can outperform the 1D-CNN-BLSTM, achieving an F-score of 94.2% for fixations, 89.9% for saccades, and 73.7% for smooth pursuits on sample level, and 89.6%, 94.3%, and 60.2% on event level. We also state the advantages of TCNs over sequential networks for this problem, and how these scores can be further improved by feature space extension.

Visualizing eye tracking data can provide insights in many research fields. However, visualizing such data efficiently and cost-effectively is challenging without well-designed tools. Easily accessible web-based approaches equipped with intuitive and interactive visualizations offer to be a promising solution. Many of such tools already exist, however, they mostly use one specific visualization technique. In this paper, we describe a web application which uses a combination of different visualization methods for eye tracking data. The visualization techniques are interactively linked to provide several perspectives on the eye tracking data. We conclude the paper by discussing challenges, limitations, and future work.

Changes in perceptional performance and attention levels in response to vibration motion stimulus in the peripheral field of vision were observed experimentally. Viewers were asked to respond to the dual tasks of detecting a single peripheral vibration while viewing a consequence task in the central field of vision. A hierarchical Bayesian model was employed to extract the features of viewing behaviour from observed response data. The estimated parameters showed the correct answer rate tendency, vibration frequency dependence, and time series for covert attention. Also, the estimated frequency of microsaccades was an indicator of the temporal change in latent attention and the suppression of eye movement.

The research aimed to compare eye movement patterns of people looking at faces with different but subtle teeth imperfections. Both non-specialists and dental experts took part in the experiment. The research outcome includes the analysis of eye movement patterns depending on the specialization, gender, age, face gender, and level of teeth deformation. The study was performed using a novel, not widely explored features of eye movements, derived from recurrence plots and Gaze Self Similarity Plots. It occurred that most features are significantly different for laypeople and specialists. Significant differences were also found for gender and age among the observers. There were no differences found when comparing the gender of the face being observed and levels of imperfection. Interestingly, it was possible to define which features are sensitive to gender and which to qualification.

Examining eye-movement measures makes understanding the intricacies of reading processes possible. Previous studies have identified some eye-movement measures such as fixation time, number of progressive and regressive saccades as possible major indices for measuring silent reading speed, however, not quite intensively and systematically investigated. The purpose of this study was to exhaustively reveal the functions of different global eye movement measures and their contribution to reading speed using linear regression analysis. Twenty-four young adults underwent an eye-tracking experiment while reading text paragraphs. Reading speed and a set of twenty-three eye-movement measures including properties of saccades, glissades and fixations were estimated. Correlation analysis indicated multicollinearity between several eye-movement measures, and accordingly, linear regression with elastic net regularization was used to model reading speed with eye-movement explanatory variables. Regression analyses revealed the capability of progressive saccade frequency and the number of progressive saccades normalized by the number of words in predicting reading speed. Furthermore, the results supported claims in the existing literature that reading speed depends on fixation duration, as well as the amplitude, number and percentage of progressive saccades, and also indicated the potential importance of glissade measures in deeper understanding of reading processes. Our findings indicate the possibility of the applied linear regression modeling approach to eventually identify important eye-movements measures related to different reading performance metrics, which could potentially improve the assessment of reading abilities.

Developmental dyslexia is a reading disability estimated to affect between 5 to 10 percent of the population. However, current screening methods are limited as they tell very little about the oculomotor processes underlying natural reading. Accordingly, investigating the eye-movement correlates of reading in a machine learning framework could potentially enhance the detec-tion of poor readers. Here, the capability of eye-movement measures in classifying dyslexic and control young adults (24 dyslexic, 24 control) was assessed on eye-tracking data acquired during reading of isolated sentences presented at five inter-letter spacing levels. The set of 65 eye-movement features included properties of fixations, saccades and glissades. Classification accuracy and importance of features were assessed for all spacing levels by aggregating the results of five feature selection methods. Highest classification accuracy (73.25%) was achieved for an increased spacing level, while the worst classification performance (63%) was obtained for the minimal spacing condition. However, the classification performance did not differ significantly between these two spacing levels (p=0.28). The most important features contributing to the best classification performance across the spacing levels were as follows: median of progressive and all saccade amplitudes, median of fixation duration and interquartile range of forward glissade duration. Selection frequency was even for the median of fixation duration, while the median amplitude of all and forward saccades measures exhibited complementary distributions across the spacing levels. The results suggest that although the importance of features may vary with the size of inter-letter spacing, the classification performance remains invariant.

Ocular proton therapy (OPT) is acknowledged as a therapeutic option for the treatment of ocular melanomas. OPT clinical workflow is deeply based on x-ray image guidance procedures, both for treatment planning and patient setup verification purposes. An optimized eye orientation relative to the proton beam axis is determined during treatment planning and it is reproduced during treatment by focusing the patient gaze on a fixation light conveniently positioned in space. Treatment geometry verification is routinely performed through stereoscopic radiographic images while real time patient gaze reproducibility is qualitatively monitored by visual control of eye surface images acquired by dedicated optical cameras. We described an approach to quantitatively evaluate the stability of patients’ gaze direction over an OPT treatment course at the National Centre of Oncological Hadrontherapy (Centro Nazionale di Adroterapia Oncologica, CNAO, Pavia, Italy). Pupil automatic segmentation procedure was implemented on eye surveillance videos of five patients recorded during OPT. Automatic pupil detection performance was benchmarked against manual pupil contours of four different clinical operators. Stability of patients’ gaze direction was quantified. 2D distances were expressed as percentage of the reference pupil radius. Valuable approximation between circular fitting and manual contours was observed. Inter-operator manual contours 2D distances were in median (interquartile range) 3.3% (3.6%) of the of the reference pupil radius. The median (interquartile range) of 2D distances between the automatic segmentations and the manual contours was 5.0% (5.3) of the of the reference pupil radius. Stability of gaze direction varied across patients with median values ranging between 6.6% and 16.5% of reference pupil radius. The measured pupil displacement on the camera field of view were clinically acceptable. Further developments are necessary to reach a real-time clip-less quantification of eye during OPT.