Search Results for author:
Found 6 papers, 2 papers with code
Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality
Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil.
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems
In remedy, we use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data, and to prune the training dataset in a manner that maximizes distribution overlap.
A Neural Active Inference Model of Perceptual-Motor Learning
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning.
EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking
Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts.
EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking
Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques.
RIT-Eyes: Rendering of near-eye images for eye-tracking applications
Deep neural networks for video-based eye tracking have demonstrated resilience to noisy environments, stray reflections, and low resolution.
