17 research outputs found
Learning to Personalize in Appearance-Based Gaze Tracking
Personal variations severely limit the performance of appearance-based gaze
tracking. Adapting to these variations using standard neural network model
adaptation methods is difficult. The problems range from overfitting, due to
small amounts of training data, to underfitting, due to restrictive model
architectures. We tackle these problems by introducing the SPatial Adaptive
GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional
latent parameter space, SPAZE provides just enough adaptability to capture the
range of personal variations without being prone to overfitting. Calibrating
SPAZE for a new person reduces to solving a small optimization problem. SPAZE
achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze,
improving on the state-of-the-art by 14 %. We contribute to gaze tracking
research by empirically showing that personal variations are well-modeled as a
3-dimensional latent parameter space for each eye. We show that this
low-dimensionality is expected by examining model-based approaches to gaze
tracking. We also show that accurate head pose-free gaze tracking is possible
Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry
Eye-tracking plays a crucial role in the development of virtual reality
devices, neuroscience research, and psychology. Despite its significance in
numerous applications, achieving an accurate, robust, and fast eye-tracking
solution remains a considerable challenge for current state-of-the-art methods.
While existing reflection-based techniques (e.g., "glint tracking") are
considered the most accurate, their performance is limited by their reliance on
sparse 3D surface data acquired solely from the cornea surface. In this paper,
we rethink the way how specular reflections can be used for eye tracking: We
propose a novel method for accurate and fast evaluation of the gaze direction
that exploits teachings from single-shot phase-measuring-deflectometry (PMD).
In contrast to state-of-the-art reflection-based methods, our method acquires
dense 3D surface information of both cornea and sclera within only one single
camera frame (single-shot). Improvements in acquired reflection surface
points("glints") of factors are easily achievable. We show the
feasibility of our approach with experimentally evaluated gaze errors of only
demonstrating a significant improvement over the current
state-of-the-art
Person Independent 3D Gaze Estimation From Remote RGB-D Cameras
We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image/gaze pairs) and infer their gaze accordingly. In this context, the paper makes three contributions: (i) unlike most previous approaches, we exploit the coupling (and constraints) between both eyes to infer their gaze jointly; (ii) we show that a generic gaze appearance model built from the aggregation of person-specific models can be used to handle unseen users and compensate for appearance variations across people, since a test user eyes' appearance will be reconstructed from similar users within the generic model. (iii) we propose an automatic model selection method that leads to comparable performance with a reduced computational load
Gaze Estimation From Multimodal Kinect Data
This paper addresses the problem of free gaze estimation under unrestricted head motion. More precisely, unlike previous approaches that mainly focus on estimating gaze towards a small planar screen, we propose a method to estimate the gaze direction in the 3D space. In this context the paper makes the following contributions: (i) leveraging on Kinect device, we propose a multimodal method that rely on depth sensing to obtain robust and accurate head pose tracking even under large head pose, and on the visual data to obtain the remaining eye-in-head gaze directional information from the eye image; (ii) a rectification scheme of the image that exploits the 3D mesh tracking, allowing to conduct a head pose free eye-in-head gaze directional estimation; (iii) a simple way of collecting ground truth data thanks to the Kinect device. Results on three users demonstrate the great potential of our approach
Towards End-to-end Video-based Eye-Tracking
Estimating eye-gaze from images alone is a challenging task, in large parts
due to un-observable person-specific factors. Achieving high accuracy typically
requires labeled data from test users which may not be attainable in real
applications. We observe that there exists a strong relationship between what
users are looking at and the appearance of the user's eyes. In response to this
understanding, we propose a novel dataset and accompanying method which aims to
explicitly learn these semantic and temporal relationships. Our video dataset
consists of time-synchronized screen recordings, user-facing camera views, and
eye gaze data, which allows for new benchmarks in temporal gaze tracking as
well as label-free refinement of gaze. Importantly, we demonstrate that the
fusion of information from visual stimuli as well as eye images can lead
towards achieving performance similar to literature-reported figures acquired
through supervised personalization. Our final method yields significant
performance improvements on our proposed EVE dataset, with up to a 28 percent
improvement in Point-of-Gaze estimates (resulting in 2.49 degrees in angular
error), paving the path towards high-accuracy screen-based eye tracking purely
from webcam sensors. The dataset and reference source code are available at
https://ait.ethz.ch/projects/2020/EVEComment: Accepted at ECCV 202
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
Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras
We propose a head pose invariant gaze estimation model for distant RGB-D cameras. It relies on a geometric understanding of the 3D gaze action and generation of eye images. By introducing a semantic segmentation of the eye region within a generative process, the model (i) avoids the critical feature tracking of geometrical approaches requiring high resolution images; (ii) decouples the person dependent geometry from the ambient conditions, allowing adaptation to different conditions without retraining. Priors in the generative framework are adequate for training from few samples. In addition, the model is capable of gaze extrapolation allowing for less restrictive training schemes. Comparisons with state of the art methods validate these properties which make our method highly valuable for addressing many diverse tasks in sociology, HRI and HCI