Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Appearance-based Eye Gaze Estimation
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Eye Gaze Estimation from a Single Image of One Eye
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A free-head, simple calibration, gaze tracking system that enables gaze-based interaction
Proceedings of the 2004 symposium on Eye tracking research & applications
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Non-intrusive eye gaze estimation without knowledge of eye pose
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Proceedings of the 2008 symposium on Eye tracking research & applications
Understanding interactions and guiding visual surveillance by tracking attention
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Evaluating the robustness of an appearance-based gaze estimation method for multimodal interfaces
Proceedings of the 15th ACM on International conference on multimodal interaction
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The purpose of this study is to develop an appearance-based method for estimating gaze directions from low resolution images. The problem of estimating directions using low resolution images is that the position of an eye region cannot be determined accurately. In this work, we introduce two key ideas to cope with the problem: incorporating training images of eye regions with artificially added positioning errors, and separating the factor of gaze variation from that of positioning error based on N-mode SVD (Singular Value Decomposition). We show that estimation of gaze direction in this framework is formulated as a bilinear problem that is then solved by alternatively minimizing a bilinear cost function with respect to gaze direction and position of the eye region. In this paper, we describe the details of our proposed method and show experimental results that demonstrate the merits of our method.