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1. Positional head-eye tracking outside the lab: an open-source solution

   https://doi.org/10.1145/3379156.3391365  

   Hausamann, P.; Sinnott, C.; MacNeilage, P.R. (June 2020, ACM Symposium on Eye Tracking Research and Applications)

   Simultaneous head and eye tracking has traditionally been confined to a laboratory setting and real-world motion tracking limited to measuring linear acceleration and angular velocity. Recently available mobile devices such as the Pupil Core eye tracker and the Intel RealSense T265 motion tracker promise to deliver accurate measurements outside the lab. Here, the researchers propose a hard- and software framework that combines both devices into a robust, usable, low-cost head and eye tracking system. The developed software is open source and the required hardware modifications can be 3D printed. The researchers demonstrate the system’s ability to measure head and eye movements in two tasks: an eyes-fixed head rotation task eliciting the vestibulo-ocular reflex inside the laboratory, and a natural locomotion task where a subject walks around a building outside of the laboratory. The resultant head and eye movements are discussed, as well as future implementations of this system. 

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2. Estimating Perceptual Depth Changes with Eye Vergence and Interpupillary Distance using an Eye Tracker in Virtual Reality

   https://doi.org/10.1145/3517031.3529632  

   Arefin, Mohammed Safayet; Swan II, J. Edward; Cohen Hoffing, Russell A.; Thurman, Steven M. (June 2022, ACM Symposium on Eye Tracking Research and Applications)

   Blascheck, Tanja; Bradshaw, Jessica; Vrzakova, Hana (Ed.)

   Virtual Reality (VR) technology has advanced to include eye-tracking, allowing novel research, such as investigating how our visual system coordinates eye movements with changes in perceptual depth. The purpose of this study was to examine whether eye tracking could track perceptual depth changes during a visual discrimination task. We derived two depth-dependent variables from eye tracker data: eye vergence angle (EVA) and interpupillary distance (IPD). As hypothesized, our results revealed that shifting gaze from near-to-far depth significantly decreased EVA and increased IPD, while the opposite pattern was observed while shifting from far-to-near. Importantly, the amount of change in these variables tracked closely with relative changes in perceptual depth, and supported the hypothesis that eye tracker data may be used to infer real-time changes in perceptual depth in VR. Our method could be used as a new tool to adaptively render information based on depth and improve the VR user experience. 

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3. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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4. Multiscale Sensor Fusion for Display-Centered Head Tracking

   https://doi.org/10.1109/VRW52623.2021.00143  

   Wu, Tianyu; Watson, Benjamin (March 2021, 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW))

   Emerging display usage scenarios require head tracking both at short (< 1m) and modest (< 3m) ranges. Yet it is difficult to find low-cost, unobtrusive tracking solutions that remain accurate across this range. By combining multiple head tracking solutions, we can mitigate the weaknesses of one solution with the strengths of another and improve head tracking overall. We built such a combination of two widely available and low-cost trackers, a Tobii Eye Tracker and a Kinect. The resulting system is more effective than Kinect at short range, and than the Tobii at a more distant range. In this paper, we discuss how we accomplish this sensor fusion and compare our combined system to an existing mechanical tracker to evaluate its accuracy across its combined range. 

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5. Aerial Border Surveillance for Search and Rescue Missions Using Eye Tracking Techniques

   https://doi.org/10.1109/THS.2018.8574165  

   Wan, Qianwen; Kaszowska, Aleksandra; Samani, Arash; Panetta, Karen; Taylor, Holly A.; Agaian, Sos (October 2018, 2018 IEEE International Symposium on Technologies for Homeland Security (HST))

   which can assure the security of the country boarder and aid in search and rescue missions. This paper offers a novel “handsfree” tool for aerial border surveillance, search and rescue missions using head-mounted eye tracking technology. The contributions of this work are: i) a gaze based aerial boarder surveillance object classification and recognition framework; ii) real-time object detection and identification system in nonscanned regions; iii) investigating the scan-path (fixation and non-scanned) provided by mobile eye tracker can help improve training professional search and rescue organizations or even artificial intelligence robots for searching and rescuing missions. The proposed system architecture is further demonstrated using a dataset of large-scale real-life head-mounted eye tracking data. Keywords—Head-mounted eye tracking technology, Aerial border surveillance, and search and rescue missions 

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