Appearance-based eye gaze estimation

By Kar-Han Tan, David J. Kriegman, Narendra Ahuja. Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings., 2002

307 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/14a55dc8a5392334eb70da2e7e491c8d952f156f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

In Human-Computer Interaction

Citations in the corpus

Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation (2014, methodology): “Such a mapping function can be learned using various regression techniques, including neural networks [2, 35], local interpolation [31, 19], or Gaussian process regression [34, 30].”
Novel Eye Gaze Tracking Techniques Under Natural Head Movement (2007, background): “In general, most of the nonintrusive vision-based gaze tracking techniques can be classified into two groups: 2-D mapping-based gaze estimation method [3], [8]–[10] and direct 3-D gaze estimation method [11]–[17].”
Gaze locking: passive eye contact detection for human-object interaction (2013, background): “Although gaze tracking has been extensively studied and current methods [2, 3, 18, 24, 36] are highly accurate, they suffer from several limitations that restrict their practical use.”
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (2017, background): “assumed a fixed head pose [37], [38], [39], [40], [41], [42],”
Appearance-based gaze estimation in the wild (2015, background): “While early works assumed a ﬁxed head pose [3, 42, 48, 35, 27, 24], recent works focused on methods for 3D head pose estimation [25, 26, 9, 6].”
Adaptive Linear Regression for Appearance-Based Gaze Estimation (2014, unknown): “' 1 -minimization has been used in previous vision-related researches, among which the works by Wright et al. [32], Wagner et al. [33] and Tan et al. [34] share similarities to ours.”
Manifold Alignment for Person Independent Appearance-Based Gaze Estimation (2014, methodology): “[8] construct an appearance manifold of training samples, specifying neighborhoods by label distances.”
Eye Tracking for Everyone (2016, unknown): “Appearance-based methods [37, 30, 22, 21, 38, 2] directly use eyes as input and can potentially work on low-resolution images.”
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking (2015, methodology): “[34] used a manifold model with densely sampled spline (252 calibration points).”
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, methodology): “One such approach employs cropped images of eyes to train regression functions, as seen in multilayer network [6], [133], [160] or Gaussian processes [157] or manifold learning [136].”
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, background): “Usually p is directly defined in the coordinate system of the target screen [17, 26, 27, 29] or, more generally, a virtual plane defined in the camera coordinate system [13].”