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Eye Tracking with Consumer Hardware

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Present commercial gaze trackers (i.e. from Tobii and LC Technology) are easy to use, robust and sufficiently accurate for many screen-based applications but their costs exceed the budget of most people. Low cost eye tracking has received an increased attention due to the rapid developments in tracking hardware (video boards, digital camera and CPUs). Eye tracking based on consumer hardware is subject to several unknown factors as various system parameters (i.e. camera parameters and geometry) are unknown. Robust statistical principles to accommodate uncertainties in image data are therefore needed. I will discuss the components (detection, tracking and gaze estimation) used in a low-cost eye tracker. I will in particular describe our contour-based iris tracker. The contour model is based on the statistics of natural images. It turns out that through fairly simple modeling that explicit feature detection can be avoided and thus thresholds become needless. Based on the data from the eye tracker I will then discuss current gaze estimation methods and compare them with gaze estimation methods using Gaussian Processes.

This talk is part of the Inference Group series.

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