The nature of statistical learning theory
The nature of statistical learning theory
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Robust Real-Time Face Detection
International Journal of Computer Vision
Texture representation based on pattern map
Signal Processing
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
Nonlinear Eye Gaze Mapping Function Estimation via Support Vector Regression
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Automated eye tracking system calibration using artificial neural networks
Computer Methods and Programs in Biomedicine
Gabor directional binary pattern: an image descriptor for gaze estimation
EURASIP Journal on Advances in Signal Processing
Computer Vision and Image Understanding
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This paper presents a novel eye gaze tracking method with allowable head movement based on a local pattern model (LPM) and support vector regressor (SVR). The LPM, a combination of improved pixel-pattern-based texture feature (PPBTF) and local-binary-pattern texture feature (LBP), is employed to calculate texture features from the characteristics of the eyes and a new binocular vision scheme is adopted to detect the spatial coordinates of the eyes. The texture features from LPM and the spatial coordinates together are fed into support vector regressor (SVR) to match a gaze mapping function, and subsequently to track gaze direction under allowable head movement. The experimental results show that the proposed approach results in better accuracy in estimating the gaze direction than the state-of-the-art pupil center corneal reflection (PCCR) method.