Gaze Tracking by Using Factorized Likelihoods Particle Filtering and Stereo Vision

This paper describes a non-intrusive method to estimate the gaze direction of a person by using stereo cameras. First, facial features are tracked with an adapted particle filtering algorithm using factorized likelihoods to estimate the 3D head pose. Next the 3D gaze vector is calculated by estimating the eyeball center and the cornea center of both eyes. For the intended application of visual perception research, we also propose a new screen registration scheme to accurately locate a planar screen in world coordinates within 2 mm error. We combine the 3D screen location with the 3D gaze vectors from both eyes to establish a point of focus on the screen. The experimental results indicate that an average error of the gaze direction of about 4.6deg can be achieved and an average error of about 4 mm for the focus point location at a viewing distance of 50 cm.