Augmented Reality (AR) devices are set apart from other mobile devices by the immersive experience they offer. Specifically, head-mounted AR devices can accurately sense and understand their environment through an increasingly powerful array of sensors such as cameras, depth sensors, eye gaze trackers, microphones, and inertial sensors. The ability of these devices to collect this information presents both challenges and opportunities to improve existing security and privacy techniques in this domain. Specifically, eye gaze tracking is a ready-made capability to analyze user intent, emotions, and vulnerability, and as an input mechanism. However, modern AR devices lack systems to address their unique security and privacy issues. Problems such as lacking local pairing mechanisms usable while immersed in AR environments, bystander privacy protections, and the increased vulnerability to shoulder surfing while wearing AR devices all lack viable solutions. In this dissertation, I explore how readily available eye gaze sensor data can be used to improve existing methods for assuring information security and protecting the privacy of those near the device. My research has presented three new systems, BystandAR, ShouldAR, and GazePair that each leverage user eye gaze to improve security and privacy expectations in or with Augmented Reality. As these devices grow in power and number, such solutions are necessary to prevent perception failures that hindered earlier devices. The work in this dissertation is presented in the hope that these solutions can improve and expedite the adoption of these powerful and useful devices. / Doctor of Philosophy / Augmented Reality (AR) devices are set apart from other mobile devices by the immersive experience they offer. The ability of these devices to collect information presents challenges and opportunities to improve existing security and privacy techniques in this domain. In this dissertation, I explore how readily available eye gaze sensor data can be used to improve existing methods for assuring security and protecting the privacy of those near the device. My research has presented three new systems, BystandAR, ShouldAR, and GazePair that each leverage user eye gaze to improve security and privacy expectations in or with Augmented Reality. As these devices grow in power and number, such solutions are necessary to prevent perception failures that hindered earlier devices. The work in this dissertation is presented in the hope that these solutions can improve and expedite the adoption of these powerful and useful devices.

This thesis describes a research study in the field of Human-Computer Interaction (HCI), specifically usability evaluation. The research investigated ways to optimise the usability of Web sites. It specifically compared Flash and HTML versions of several different types of Web sites. The study commenced with a literature review regarding the process of usability evaluation of Web sites. Various usability evaluation methods and techniques were explored, and two emerging techniques were chosen for further investigation: (1) a contingent heuristic approach; and (2) eye gaze tracking. In order to confirm that these two techniques can be used effectively for Web site usability evaluations, two experiments were conducted to evaluate the usability of Web sites. The first experiment utilised an online questionnaire derived from the Website Usability Contingent Evaluation Tool (WUCET), which was based on the contingent heuristic approach. The second experiment involved eye gaze tracking with the faceLAB system, while participants interacted with Web sites of different types. Both experiments utilised Flash and HTML versions of the same set of Web sites. By analysing data collected from the experiments, comparisons between the usability of Flash and HTML versions were made. The results from quantitative and qualitative analyses of survey responses suggested that Flash version of Web sites, in general, provided higher usability than HTML version of Web sites, but eye gaze tracking data analyses showed no significant difference between the two versions. However, analyses of the eye tracking data were useful for improving understanding of the ways in which users interact with different versions of the Web sites. In addition, other influential factors that could affect the perceived usability of the Web sites, such as user’s gender and previous experience with computers and the Web, were also considered. The results of the experiments showed that in regard to Flash and HTML implementations of Web sites, there was a difference in Web site usability perception patterns between male and female users, and also between users with long-term computer/Web experience and users with short-term experience. In addition, a range of different types (purposes) of Web sites were utilised. In this study, selected Web sites fall into three broad categories according to their main purpose: (1) information; (2) entertainment; and (3) e-commerce. It was discovered that the type of Web sites also influenced the usability of Flash and HTML versions of Web sites, as perceived by users.

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In this thesis, a visual 3-D eye gaze tracker is designed and implemented to tested via computer simulations and on an experimental setup. Proposed tracker is designed to examine human perception on autostereoscopic displays when the viewer is 3m away from such displays. Two different methods are proposed for calibrating personal parameters and gaze estimation, namely line of gaze (LoG) and line of sight (LoS) solutions. 2-D and 3-D estimation performances of the proposed system are observed both using computer simulations and the experimental setup. In terms of 2-D and 3-D performance criteria, LoS solution generates slightly better results compared to that of LoG on experimental setup and their performances are found to be comparable in simulations. 2-D estimation inaccuracy of the system is obtained as smaller than 0.5&deg / during simulations and approximately 1&deg / for the experimental setup. 3-D estimation inaccuracy of the system along x- and y-axis is obtained as smaller than 2&deg / during the simulations and the experiments. However, estimation accuracy along z-direction is significantly sensitive to pupil detection and head pose estimation errors. For typical error levels, 20cm inaccuracy along z-direction is observed during simulations, whereas this inaccuracy reaches 80cm in the experimental setup.

Indiana University-Purdue University Indianapolis (IUPUI) / One of the biggest hurdles to the development of an effective driver state monitor is the that there is no real-time eye-gaze detection. This is primarily due to the fact that such systems require calibration. In this thesis the various aspects that comprise an eye gaze tracker are investigated. From that we developed an eye gaze tracker for automobiles that does not require calibration. We used a monocular camera system with IR light sources placed in each of the three mirrors. The camera system created the bright-pupil effect for robust pupil detection and tracking. We developed an SVM based algorithm for initial eye candidate detection; after that the eyes were tracked using a hybrid Kalman/Mean-shift algorithm. From the tracked pupils, various features such as the location of the glints (reflections in the pupil from the IR light sources) were extracted. This information is then fed into a Generalized Regression Neural Network (GRNN). The GRNN then maps this information into one of thirteen gaze regions in the vehicle.

This thesis deals with the eye gaze tracking and determining the direction and the place of the view. The overview of methods for determining the view direction is provided here. The direct 3D gaze estimation technique is proposed and implemented. The eye gaze can be estimated by using a single camera and under nature head movement. This paper describes the algorithms for the image processing and the image description. The center of the pupil is determined by the genetic algorithm for the ellipse detection. The procedures of the instrumentation calibration are proposed here as well. The work also addresses the issue of the determining the eye location. Finally, the comparison of the accuracy with similar projects is mentioned in my thesis.

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation and training of new eye-gaze tracking datasets. As we explored gaze patterns for various user-performed tasks, we developed generalizable eye-gaze metrics that capture and reflect the distinct gaze trends among different cohorts. And through testing various feature extraction and classification methods, we have found promising results that have enabled us to effectively classify individuals with Mild Neurocognitive Disorder (MiNCD) / Mild Cognitive Impairment (MCI) in a crowdsourced pilot study (N = 35) with an average accuracy of 0.94 (f1 = 0.83). Although just the beginning, this work represents the first step towards establishing predictive eye-gaze tracking as an accessible and important modality for healthcare applications moving forward, with the potential to significantly impact remote screening and monitoring of neurocognitive health.