Comparison of head gaze and head and eye gaze within an immersive environment

For efficient collaboration between participants, eye gaze is seen as being critical for interaction. Teleconferencing systems such as the AcessGrid allow users to meet across geographically disparate rooms but as of now there seems no substitute for face to face meetings. This paper gives an overview of some preliminary work that looks towards integrating eye gaze into an immersive Collaborative Virtual Environment and assessing the impact that this would have on interaction between the users of such a system. An experiment was conducted to assess the difference between users abilities to judge what objects an avatar is looking at with only head gaze being viewed and also with eye and head gaze data being displayed. The results from the experiment show that eye gaze is of vital importance to the subjects correctly identifying what a person is looking at in an immersive virtual environment. This is followed by a description of how the eye tracking system has been integrated into an immersive collaborative virtual environment and some preliminary results from the use of such a system

Robust Real-Time Multi-View Eye Tracking

Despite significant advances in improving the gaze tracking accuracy under controlled conditions, the tracking robustness under real-world conditions, such as large head pose and movements, use of eyeglasses, illumination and eye type variations, remains a major challenge in eye tracking. In this paper, we revisit this challenge and introduce a real-time multi-camera eye tracking framework to improve the tracking robustness. First, differently from previous work, we design a multi-view tracking setup that allows for acquiring multiple eye appearances simultaneously. Leveraging multi-view appearances enables to more reliably detect gaze features under challenging conditions, particularly when they are obstructed in conventional single-view appearance due to large head movements or eyewear effects. The features extracted on various appearances are then used for estimating multiple gaze outputs. Second, we propose to combine estimated gaze outputs through an adaptive fusion mechanism to compute user's overall point of regard. The proposed mechanism firstly determines the estimation reliability of each gaze output according to user's momentary head pose and predicted gazing behavior, and then performs a reliability-based weighted fusion. We demonstrate the efficacy of our framework with extensive simulations and user experiments on a collected dataset featuring 20 subjects. Our results show that in comparison with state-of-the-art eye trackers, the proposed framework provides not only a significant enhancement in accuracy but also a notable robustness. Our prototype system runs at 30 frames-per-second (fps) and achieves 1 degree accuracy under challenging experimental scenarios, which makes it suitable for applications demanding high accuracy and robustness.Comment: Organisational changes in the main msp and supplementary info. Results unchanged. Main msp: 14 pages, 15 figures. Supplementary: 2 tables, 1 figure. Under review for an IEEE transactions publicatio

OpenEDS: Open Eye Dataset

We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on OpenEDS for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersectionover-union (mIoU) of 98.3 %. We anticipate that OpenEDS will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications. The dataset is available for download upon request at https://research.fb.com/programs/openeds-challengeComment: 11 pages; 12 figure

A Computer Vision System for Attention Mapping in SLAM based 3D Models

The study of human factors in the frame of interaction studies has been relevant for usability engi-neering and ergonomics for decades. Today, with the advent of wearable eye-tracking and Google glasses, monitoring of human factors will soon become ubiquitous. This work describes a computer vision system that enables pervasive mapping and monitoring of human attention. The key contribu-tion is that our methodology enables full 3D recovery of the gaze pointer, human view frustum and associated human centred measurements directly into an automatically computed 3D model in real-time. We apply RGB-D SLAM and descriptor matching methodologies for the 3D modelling, locali-zation and fully automated annotation of ROIs (regions of interest) within the acquired 3D model. This innovative methodology will open new avenues for attention studies in real world environments, bringing new potential into automated processing for human factors technologies.Comment: Part of the OAGM/AAPR 2013 proceedings (arXiv:1304.1876

Eye Gaze Controlled Interfaces for Head Mounted and Multi-Functional Displays in Military Aviation Environment

Eye gaze controlled interfaces allow us to directly manipulate a graphical user interface just by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots hands are occupied with flying the aircraft. This paper reports studies on analyzing accuracy of eye gaze controlled interface inside aircraft undertaking representative flying missions. We reported that pilots can undertake representative pointing and selection tasks at less than 2 secs on average. Further, we evaluated the accuracy of eye gaze tracking glass under various G-conditions and analyzed its failure modes. We observed that the accuracy of an eye tracker is less than 5 degree of visual angle up to +3G, although it is less accurate at minus 1G and plus 5G. We observed that eye tracker may fail to track under higher external illumination. We also infer that an eye tracker to be used in military aviation need to have larger vertical field of view than the present available systems. We used this analysis to develop eye gaze trackers for Multi-Functional displays and Head Mounted Display System. We obtained significant reduction in pointing and selection times using our proposed HMDS system compared to traditional TDS.Comment: Presented at IEEE Aerospace 202

Free-View, 3D Gaze-Guided, Assistive Robotic System for Activities of Daily Living

Patients suffering from quadriplegia have limited body motion which prevents them from performing daily activities. We have developed an assistive robotic system with an intuitive free-view gaze interface. The user's point of regard is estimated in 3D space while allowing free head movement and is combined with object recognition and trajectory planning. This framework allows the user to interact with objects using fixations. Two operational modes have been implemented to cater for different eventualities. The automatic mode performs a pre-defined task associated with a gaze-selected object, while the manual mode allows gaze control of the robot's end-effector position on the user's frame of reference. User studies reported effortless operation in automatic mode. A manual pick and place task achieved a success rate of 100% on the users' first attempt.Comment: 7 Pages, 9 Figures, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), Madrid, Spai

Noninvasive Corneal Image-Based Gaze Measurement System

Gaze tracking is an important technology as the system can give information about a person from what and where the person is seeing. There have been many attempts to make robust and accurate gaze trackers using either monitor or wearable devices. However, those contraptions often require fine individual calibration per session and/or require a person wearing a device, which may not be suitable for certain situations. In this paper, we propose a robust and a completely noninvasive gaze tracking system that involves neither complex calibrations nor the use of wearable devices. We achieve this via direct eye reflection analysis by building a real-time system that effectively enables it. We also show several interesting applications for our system including experiments with young children

Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping

Assistive robotic systems endeavour to support those with movement disabilities, enabling them to move again and regain functionality. Main issue with these systems is the complexity of their low-level control, and how to translate this to simpler, higher level commands that are easy and intuitive for a human user to interact with. We have created a multi-modal system, consisting of different sensing, decision making and actuating modalities, leading to intuitive, human-in-the-loop assistive robotics. The system takes its cue from the user's gaze, to decode their intentions and implement low-level motion actions to achieve high-level tasks. This results in the user simply having to look at the objects of interest, for the robotic system to assist them in reaching for those objects, grasping them, and using them to interact with other objects. We present our method for 3D gaze estimation, and grammars-based implementation of sequences of action with the robotic system. The 3D gaze estimation is evaluated with 8 subjects, showing an overall accuracy of $4.68\pm0.14cm$. The full system is tested with 5 subjects, showing successful implementation of $100\%$ of reach to gaze point actions and full implementation of pick and place tasks in 96\%, and pick and pour tasks in $76\%$ of cases. Finally we present a discussion on our results and what future work is needed to improve the system.Comment: 7 pages, 7 figures, 4 tables. Submitted to IEEE ICRA 2019 - under revie

Eyemotion: Classifying facial expressions in VR using eye-tracking cameras

One of the main challenges of social interaction in virtual reality settings is that head-mounted displays occlude a large portion of the face, blocking facial expressions and thereby restricting social engagement cues among users. Hence, auxiliary means of sensing and conveying these expressions are needed. We present an algorithm to automatically infer expressions by analyzing only a partially occluded face while the user is engaged in a virtual reality experience. Specifically, we show that images of the user's eyes captured from an IR gaze-tracking camera within a VR headset are sufficient to infer a select subset of facial expressions without the use of any fixed external camera. Using these inferences, we can generate dynamic avatars in real-time which function as an expressive surrogate for the user. We propose a novel data collection pipeline as well as a novel approach for increasing CNN accuracy via personalization. Our results show a mean accuracy of 74% ($F1$ of 0.73) among 5 `emotive' expressions and a mean accuracy of 70% ($F1$ of 0.68) among 10 distinct facial action units, outperforming human raters.Comment: Uploaded Supplementary PDF. Fixed author affiliation. Corrected typo in personalization accurac

OpenEDS2020: Open Eyes Dataset

We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. Baseline experiments have been evaluated on OpenEDS2020, one for each task, with average angular error of 5.37 degrees when performing gaze prediction on 1 to 5 frames into the future, and a mean intersection over union score of 84.1% for semantic segmentation. As its predecessor, OpenEDS dataset, we anticipate that this new dataset will continue creating opportunities to researchers in eye tracking, machine learning and computer vision communities, to advance the state of the art for virtual reality applications. The dataset is available for download upon request at http://research.fb.com/programs/openeds-2020-challenge/.Comment: Description of dataset used in OpenEDS2020 challenge: https://research.fb.com/programs/openeds-2020-challenge