19,961 research outputs found
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
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
EyeScout: Active Eye Tracking for Position and Movement Independent Gaze Interaction with Large Public Displays
While gaze holds a lot of promise for hands-free interaction with public displays, remote eye trackers with their confined tracking box restrict users to a single stationary position in front of the display. We present EyeScout, an active eye tracking system that combines an eye tracker mounted on a rail system with a computational method to automatically detect and align the tracker with the user's lateral movement. EyeScout addresses key limitations of current gaze-enabled large public displays by offering two novel gaze-interaction modes for a single user: In "Walk then Interact" the user can walk up to an arbitrary position in front of the display and interact, while in "Walk and Interact" the user can interact even while on the move. We report on a user study that shows that EyeScout is well perceived by users, extends a public display's sweet spot into a sweet line, and reduces gaze interaction kick-off time to 3.5 seconds -- a 62% improvement over state of the art solutions. We discuss sample applications that demonstrate how EyeScout can enable position and movement-independent gaze interaction with large public displays
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