A video eye tracking system based on a statistical algorithm

Presents the design and analysis of an algorithm which determines the yaw, pitch, roll and pupil diameter states of an eye viewed with a standard video camera. A maximum likelihood estimation technique tracks the location and size of the pupil in a video image to find horizontal and vertical eye position. Simulations and analyses show that the noiseless measuring resolution of horizontal and vertical movements is less than 0.05 pixel on an image. Eased on accurate measurements of pupil position, counterroll movements are calculated using cross correlations between one dimensional templates which consist of equidistant pixels on a partial annulus overlying the iris and concentric with the pupil center. Another advantage of the algorithm is a robustness with respect to intrusions of droopy eyelids and random light reflections. Analysis shows that eyelids which cover pupils by less than a third of pupil radius do nor cause a bias in pupil position estimates. Light reflections on the pupil boundary have a minimal effect on estimate bias, while light reflections embedded inside the pupil have a lesser effect. The speed of image analysis (about 10 frames per second on Macintosh IIfx computer), the robustness for eyelid cover and random light reflections, and the ability to track 4 dimensional eye movement (horizontal, vertical, counterroll movement and pupil size) are major characteristics of the algorithm