Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Task oriented facial behavior recognition with selective sensing
Computer Vision and Image Understanding
Facial event classification with task oriented dynamic Bayesian network
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An automated face reader for fatigue detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Kernel-Based robust tracking for objects undergoing occlusion
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Visual tracking by adaptive kalman filtering and mean shift
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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Most eye trackers based on active IR illumination requir distinctive bright pupil effect to work well. However, due to a variety of factors such as eye closure, eye occlusion, and external illuminations interference, pupils are not bright enough for these methods to work well. This tends to significantly limit their scope of application. In this paper, we present a new real time eye tracking methodology that works under variable and realistic lighting conditions and various face orientations. By combining the conventional appearance based object recognition method (SVM) and object tracking method (mean shift) with Kalaman filtering based on active IR illumination, our technique is able to benefit from the strengths of different techniques and overcome their respective limitations. Experimental study shows significant improvement of our technique over the existing techniques.