Feature extraction from faces using deformable templates
International Journal of Computer Vision
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A deformable model of the human iris for measuring small three-dimensional eye movements
Machine Vision and Applications
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
Effective eye-gaze input into Windows
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
GAZE-2: an attentive video conferencing system
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Differences in the infrared bright pupil response of human eyes
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Focusing on the essential: considering attention in display design
Communications of the ACM
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Edge and Keypoint Detection in Facial Regions
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Gaze-orchestrated dynamic windows
SIGGRAPH '81 Proceedings of the 8th annual conference on Computer graphics and interactive techniques
Eye Typing using Markov and Active Appearance Models
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Fast Radial Symmetry for Detecting Points of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Gaze typing compared with input by head and hand
Proceedings of the 2004 symposium on Eye tracking research & applications
A novel non-intrusive eye gaze estimation using cross-ratio under large head motion
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Computer Vision and Image Understanding - Special issue on eye detection and tracking
A generative framework for real time object detection and classification
Computer Vision and Image Understanding - Special issue on eye detection and tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Driver State Monitor from DELPHI
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Computer Vision and Image Understanding
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While existing eye detection and tracking algorithms can work reasonably well in a controlled environment, they tend to perform poorly under real world imaging conditions where the lighting produces shadows and the person's eyes can be occluded by e.g. glasses or makeup. As a result, pixel clusters associated with the eyes tend to be grouped together with background-features. This problem occurs both for eye detection and eye tracking. Problems that especially plague eye tracking include head movement, eye blinking and light changes, all of which can cause the eyes to suddenly disappear. The usual approach in such cases is to abandon the tracking routine and re-initialize eye detection. Of course this may be a difficult process due to missed data problem. Accordingly, what is needed is an efficient method of reliably tracking a person's eyes between successively produced video image frames, even in situations where the person's head turns, the eyes momentarily close and/or the lighting conditions are variable. The present paper is directed to an efficient and reliable method of tracking a human eye between successively produced infrared interlaced image frames where the lighting conditions are challenging. It proposes a log likelihood-ratio function of foreground and background models in a particle filter-based eye tracking framework. It fuses key information from even, odd infrared fields (dark and bright-pupil) and their corresponding subtractive image into one single observation model. Experimental validations show good performance of the proposed eye tracker in challenging conditions that include moderate head motion and significant local and global lighting changes. The paper presents also an eye detector that relies on physiological infrared eye responses and a modified version of a cascaded classifier.