|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
NON-INTRUSIVE GAZE TRACKING FOR HUMAN-COMPUTER INTERACTION
Andrew Gee and Roberto Cipolla
Current approaches to gaze tracking tend to be highly intrusive: the subject must either remain perfectly still, or wear cumbersome headgear to maintain a constant separation between the sensor and the eye. This paper describes a more flexible vision-based approach, which can estimate the direction of gaze from a single, monocular view of a face. The technique makes minimal assumptions about the structure of the face, requires very few image measurements, and produces a useful estimate of the facial orientation. The computational requirements are insignificant, so with automatic tracking of a few facial features it is possible to produce real-time gaze estimates. A robust, multiple hypothesis tracker is described, which utilises no expensive correlation operations and runs at video rate on standard hardware.
Keywords: Gaze tracking, human-computer interaction, weak perspective, affine camera, real-time feature tracking.
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