Non-Intrusive Gaze Tracking Using Artificial Neural Networks
Non-Intrusive Gaze Tracking Using Artificial Neural Networks
Eye Gaze Tracking under Natural Head Movements
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse and Semi-supervised Visual Mapping with the S^3GP
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
An Incremental Learning Method for Unconstrained Gaze Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrimination of gaze directions using low-level eye image features
Proceedings of the 1st international workshop on pervasive eye tracking & mobile eye-based interaction
SideWays: a gaze interface for spontaneous interaction with situated displays
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Eye pupil localization with an ensemble of randomized trees
Pattern Recognition
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We contribute a novel gaze estimation technique, which is adaptable for person-independent applications. In a study with 17 participants, using a standard webcam, we recorded the subjects' left eye images for different gaze locations. From these images, we extracted five types of basic visual features. We then sub-selected a set of features with minimum Redundancy Maximum Relevance (mRMR) for the input of a 2-layer regression neural network for estimating the subjects' gaze. We investigated the effect of different visual features on the accuracy of gaze estimation. Using machine learning techniques, by combing different features, we achieved average gaze estimation error of 3.44° horizontally and 1.37° vertically for person-dependent.