Universität Bielefeld - Sonderforschungsbereich 360

An Artificial Neural Network for High Precision Eye Movement Tracking

Marc Pomplun, Helge Ritter, Boris Velichkovsky

Abstract

Research of visual cognition often suffers from very inexact methods of eye movement recording. A so-called eye tracker, fastened to the test person's head, yields information about pupil position and facing direction related to a computer monitor in front of the subject. It is now a software task to calculate the coordinates of the screen point the person is looking at. Conventional algorithms are not able to realize the required non-linear projection very precisely. Especially if the test person is wearing spectacles, the deviation may exceed 3 degrees of visual angle. In this paper a new approach is presented, solving the problem with a parametrized self-organizing map (PSOM). After a short calibration it reduces the average error to approximately 30 percent of its initial value. Due to its high efficiency (less than 150 micrometers per computation on a PC with a 486DX2-66 processor) it is perfectly suited for real-time application.

Postscript-File (~ 89 k)

Anke Weinberger, 1995-02-23, 1995-09-25