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Invariant Descriptors for 3D Object Recognition and Pose
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Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
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Gaze Estimation Using Morphable Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Real-Time Stereo Tracking for Head Pose and Gaze Estimation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
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ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
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Computer Vision and Image Understanding - Special issue on eye detection and tracking
Eyes segmentation applied to gaze direction and vigilance estimation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Real-time eye-gaze estimation using a low-resolution webcam
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Eye gaze estimation via images has been well investigated and all methods worked on images with the full face. This makes the eye details small and accuracy is affected. We address this problem by zooming in on a single eye. In this paper, we will show the validity of the method and investigate the performance with controlled synthetic data and also with real images. The principle is to rely on the fact that the outer boundary of the iris is a circle. With a fully calibrated camera, its elliptical image can be back-projected into the 3D space yielding two possible circles. To disambiguate, the solution is found by making use of anthropomorphic knowledge of the structure of the eyeball. Hence, getting a larger eye image with a zoom camera enabled us to achieve higher resolutions and thereby higher accuracies. The robustness of the algorithm was verified by extensive statistical trials conducted on synthetic data and real images. The two key contributions in this paper are to show that it is possible to estimate eye gaze with only one eye image and that consequently this achieves higher accuracy of eye gaze estimation.