Large eye-head gaze shifts measured with a wearable eye tracker and an industrial camera.

We built a novel setup to record large gaze shifts (up to 140 ∘ ). The setup consists of a wearable eye tracker and a high-speed camera with fiducial marker technology to track the head. We tested our setup by replicating findings from the classic eye-head gaze shift literature. We conclude that our new inexpensive setup is good enough to investigate the dynamics of large eye-head gaze shifts. This novel setup could be used for future research on large eye-head gaze shifts, but also for research on gaze during e.g., human interaction. We further discuss reference frames and terminology in head-free eye tracking. Despite a transition from head-fixed eye tracking to head-free gaze tracking, researchers still use head-fixed eye movement terminology when discussing world-fixed gaze phenomena. We propose to use more specific terminology for world-fixed phenomena, including gaze fixation, gaze pursuit, and gaze saccade.

Noise estimation for head-mounted 3D binocular eye tracking using Pupil Core eye-tracking goggles.

Head-mounted, video-based eye tracking is becoming increasingly common and has promise in a range of applications. Here, we provide a practical and systematic assessment of the sources of measurement uncertainty for one such device - the Pupil Core - in three eye-tracking domains: (1) the 2D scene camera image; (2) the physical rotation of the eye relative to the scene camera 3D space; and (3) the external projection of the estimated gaze point location onto the target plane or in relation to world coordinates. We also assess eye camera motion during active tasks relative to the eye and the scene camera, an important consideration as the rigid arrangement of eye and scene camera is essential for proper alignment of the detected gaze. We find that eye camera motion, improper gaze point depth estimation, and erroneous eye models can all lead to added noise that must be considered in the experimental design. Further, while calibration accuracy and precision estimates can help assess data quality in the scene camera image, they may not be reflective of errors and variability in gaze point estimation. These findings support the importance of eye model constancy for comparisons across experimental conditions and suggest additional assessments of data reliability may be warranted for experiments that require the gaze point or measure eye movements relative to the external world.

How robust are wearable eye trackers to slow and fast head and body movements?

How well can modern wearable eye trackers cope with head and body movement? To investigate this question, we asked four participants to stand still, walk, skip, and jump while fixating a static physical target in space. We did this for six different eye trackers. All the eye trackers were capable of recording gaze during the most dynamic episodes (skipping and jumping). The accuracy became worse as movement got wilder. During skipping and jumping, the biggest error was 5.8∘. However, most errors were smaller than 3∘. We discuss the implications of decreased accuracy in the context of different research scenarios.

Exploring Eye Movement Biometrics in Real-World Activities: A Case Study of Wayfinding.

Eye movement biometrics can enable continuous verification for highly secure environments such as financial transactions and defense establishments, as well as a more personalized and tailored experience in gaze-based human-computer interactions. However, there are numerous challenges to recognizing people in real environments using eye movements, such as implicity and stimulus independence. In the instance of wayfinding, this research intends to investigate implicit and stimulus-independent eye movement biometrics in real-world situations. We collected 39 subjects' eye movement data from real-world wayfinding experiments and derived five sets of eye movement features (the basic statistical, pupillary response, fixation density, fixation semantic and saccade encoding features). We adopted a random forest and performed biometric recognition for both identification and verification scenarios. The best accuracy we obtained in the identification scenario was 78% (equal error rate, EER = 6.3%) with the 10-fold classification and 64% (EER = 12.1%) with the leave-one-route-out classification. The best accuracy we achieved in the verification scenario was 89% (EER = 9.1%). Additionally, we tested performance across the 5 feature sets and 20 time window sizes. The results showed that the verification accuracy was insensitive to the increase in the time window size. These findings are the first indication of the viability of performing implicit and stimulus-independent biometric recognition in real-world settings using wearable eye tracking.

The impact of slippage on the data quality of head-worn eye trackers.

Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant's head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs' Pupil in 3D mode, and (iv) Pupil-Labs' Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8-3.1∘ increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup.

GlassesViewer: Open-source software for viewing and analyzing data from the Tobii Pro Glasses 2 eye tracker.

We present GlassesViewer, open-source software for viewing and analyzing eye-tracking data of the Tobii Pro Glasses 2 head-mounted eye tracker as well as the scene and eye videos and other data streams (pupil size, gyroscope, accelerometer, and TTL input) that this headset can record. The software provides the following functionality written in MATLAB: (1) a graphical interface for navigating the study- and recording structure produced by the Tobii Glasses 2; (2) functionality to unpack, parse, and synchronize the various data and video streams comprising a Glasses 2 recording; and (3) a graphical interface for viewing the Glasses 2's gaze direction, pupil size, gyroscope and accelerometer time-series data, along with the recorded scene and eye camera videos. In this latter interface, segments of data can furthermore be labeled through user-provided event classification algorithms or by means of manual annotation. Lastly, the toolbox provides integration with the GazeCode tool by Benjamins et al. (2018), enabling a completely open-source workflow for analyzing Tobii Pro Glasses 2 recordings.

Task-related gaze control in human crowd navigation.

Human crowds provide an interesting case for research on the perception of people. In this study, we investigate how visual information is acquired for (1) navigating human crowds and (2) seeking out social affordances in crowds by studying gaze behavior during human crowd navigation under different task instructions. Observers (n = 11) wore head-mounted eye-tracking glasses and walked two rounds through hallways containing walking crowds (n = 38) and static objects. For round one, observers were instructed to avoid collisions. For round two, observers furthermore had to indicate with a button press whether oncoming people made eye contact. Task performance (walking speed, absence of collisions) was similar across rounds. Fixation durations indicated that heads, bodies, objects, and walls maintained gaze comparably long. Only crowds in the distance maintained gaze relatively longer. We find no compelling evidence that human bodies and heads hold one's gaze more than objects while navigating crowds. When eye contact was assessed, heads were fixated more often and for a total longer duration, which came at the cost of looking at bodies. We conclude that gaze behavior in crowd navigation is task-dependent, and that not every fixation is strictly necessary for navigating crowds. When explicitly tasked with seeking out potential social affordances, gaze is modulated as a result. We discuss our findings in the light of current theories and models of gaze behavior. Furthermore, we show that in a head-mounted eye-tracking study, a large degree of experimental control can be maintained while many degrees of freedom on the side of the observer remain.