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Once the eye region is detected, the iris center can be obtained using conventional pupil detection and integrodifferential approach for finding the iris boundary and a static boundary can be fitted.
The tracking phase incorporates skin color segmentation, black pupil detection and support vector machines.
The details of the system and the pupil detection procedure are explained.
Because some images were misdetected due to noise caused by a low frame rate of IR camera and occlusion by eyelids and eyelashes, the relative high miss rate of pupil detection and the IQR errors was shown.
Most researchers only make use of the difference image for pupil detection; however, in real driving conditions, this image deteriorates due to external illumination, vibrations, and so forth and is also very sensitive to lighting conditions.
If participants have long eyelashes or narrow eyes, GazeParser may encounter difficulty in pupil detection, as with other video-based eyetrackers, and, therefore, narrow the tracking range.
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Thus, this method was used for pupil movement detection.
We show in this paper an approach for joint pupil boundary detection and fit that achieves reliable tracking results under adverse recording conditions, overcoming consistently these prevalent artifacts.
The larger window size causes larger elliptical fittings with higher accuracy in terms of pupil-center detection while the center of the ellipse is considered as the pupil-center.
In this article, a shape- and intensity-based deformable eye pupil-center detection and movement decision algorithms are introduced in terms of developing a low-cost 2D gaze estimation and cursor control system regardless of the real position of pupil-center.
The detection pupil P2 is simply given by (14) P 2 = 1.
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