941 research outputs found
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
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world
settings, but existing datasets have been collected under controlled laboratory
conditions and methods have been not evaluated across multiple datasets. In
this work we study appearance-based gaze estimation in the wild. We present the
MPIIGaze dataset that contains 213,659 images we collected from 15 participants
during natural everyday laptop use over more than three months. Our dataset is
significantly more variable than existing ones with respect to appearance and
illumination. We also present a method for in-the-wild appearance-based gaze
estimation using multimodal convolutional neural networks that significantly
outperforms state-of-the art methods in the most challenging cross-dataset
evaluation. We present an extensive evaluation of several state-of-the-art
image-based gaze estimation algorithms on three current datasets, including our
own. This evaluation provides clear insights and allows us to identify key
research challenges of gaze estimation in the wild
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
When Computer Vision Gazes at Cognition
Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.Comment: Tao Gao and Daniel Harari contributed equally to this wor
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
A framework for realistic 3D tele-immersion
Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems
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
An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe
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