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Found 13 papers, 3 papers with code
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.
Semi-Supervised Learning for Eye Image Segmentation
Recent advances in appearance-based models have shown improved eye tracking performance in difficult scenarios like occlusion due to eyelashes, eyelids or camera placement, and environmental reflections on the cornea and glasses.
$pi_t$- Enhancing the Precision of Eye Tracking using Iris Feature Motion Vectors
A new high-precision eye-tracking method has been demonstrated recently by tracking the motion of iris features rather than by exploiting pupil edges.
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.
Privacy-Preserving Eye Videos using Rubber Sheet Model
Video-based eye trackers estimate gaze based on eye images/videos.
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time.
Ranked #1 on
Semantic Segmentation
on OpenEDS
SNAG: Spoken Narratives and Gaze Dataset
Humans rely on multiple sensory modalities when examining and reasoning over images.
