13,457 research outputs found
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
Towards Precision in Appearance-based Gaze Estimation in the Wild
Appearance-based gaze estimation systems have shown great progress recently,
yet the performance of these techniques depend on the datasets used for
training. Most of the existing gaze estimation datasets setup in interactive
settings were recorded in laboratory conditions and those recorded in the wild
conditions display limited head pose and illumination variations. Further, we
observed little attention so far towards precision evaluations of existing gaze
estimation approaches. In this work, we present a large gaze estimation
dataset, PARKS-Gaze, with wider head pose and illumination variation and with
multiple samples for a single Point of Gaze (PoG). The dataset contains 974
minutes of data from 28 participants with a head pose range of 60 degrees in
both yaw and pitch directions. Our within-dataset and cross-dataset evaluations
and precision evaluations indicate that the proposed dataset is more
challenging and enable models to generalize on unseen participants better than
the existing in-the-wild datasets. The project page can be accessed here:
https://github.com/lrdmurthy/PARKS-Gaz
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
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
Rendering of eyes for eye-shape registration and gaze estimation
© 2015 IEEE. Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild
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