973 research outputs found
Unobtrusive and pervasive video-based eye-gaze tracking
Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe
Robust eye tracking based on multiple corneal reflections for clinical applications
Postprint (published version
High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods
This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range
Low-cost eye tracking calibration: a knowledge-based study
Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking system
Models for gaze tracking systems
One of the most confusing aspects that one meets when introducing oneself into gaze tracking technology is the wide variety, in terms of hardware equipment, of available systems that provide solutions to the same matter, that is, determining the point the subject is looking at. The calibration process permits generally adjusting nonintrusive trackers based on quite different hardware and image features to the subject. The negative aspect of this simple procedure is that it permits the system to work properly but at the expense of a lack of control over the intrinsic behavior of the tracker. The objective of the presented article is to overcome this obstacle to explore more deeply the elements of a video-oculographic system, that is, eye, camera, lighting, and so forth, from a purely mathematical and geometrical point of view. The main contribution is to find out the minimum number of hardware elements and image features that are needed to determine the point the subject is looking at. A model has been constructed based on pupil contour and multiple lighting, and successfully tested with real subjects. On the other hand, theoretical aspects of video-oculographic systems have been thoroughly reviewed in order to build a theoretical basis for further studies
A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
In this paper a review is presented of the research on eye gaze estimation
techniques and applications, that has progressed in diverse ways over the past
two decades. Several generic eye gaze use-cases are identified: desktop, TV,
head-mounted, automotive and handheld devices. Analysis of the literature leads
to the identification of several platform specific factors that influence gaze
tracking accuracy. A key outcome from this review is the realization of a need
to develop standardized methodologies for performance evaluation of gaze
tracking systems and achieve consistency in their specification and comparative
evaluation. To address this need, the concept of a methodological framework for
practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July
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Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
Conventional feature-based and model-based gaze estimation methods have
proven to perform well in settings with controlled illumination and specialized
cameras. In unconstrained real-world settings, however, such methods are
surpassed by recent appearance-based methods due to difficulties in modeling
factors such as illumination changes and other visual artifacts. We present a
novel learning-based method for eye region landmark localization that enables
conventional methods to be competitive to latest appearance-based methods.
Despite having been trained exclusively on synthetic data, our method exceeds
the state of the art for iris localization and eye shape registration on
real-world imagery. We then use the detected landmarks as input to iterative
model-fitting and lightweight learning-based gaze estimation methods. Our
approach outperforms existing model-fitting and appearance-based methods in the
context of person-independent and personalized gaze estimation
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