Eye Movement Tracking using Radial Basis Function Networks

Abstract

This paper proposes a Radial Basis Function Network (RBFN) for the path way of eye progress. Depends on space and time the eye progress information is extracted from the video through this technique. The tracking of eye motion is dominant and worked out by the RBFN from the transmitter to receiver end. From this information, the pedestal pattern is twisted to create a calculation pattern. The eye progress calculation is customized using a resolution based advance, with a entry to distinguish the various condition of eyes like eyes totally open, eyes partially open or partially closed and eyes totally closed between the consecutive borders. The precision of the eye progress is broadcasted with the help of number of nodules in the unseen coating and erudition feature.  The number of nodules in the unseen coating is 49, and the erudition feature is 0.9. In rebuilding the path way of eye progress accurately, is about 97.45%.