Towards a subject-independent adaptive pupil tracker for automatic eye tracking calibration using a mixture model

Abstract

This paper describes the initial pre-processing steps used to follow the motions of the human eye in an eye tracking application. The central method models each pixel as a combination of either: a dark pupil pixel, bright highlight pixel, or a neutral pixel. Portable eye tracking involves tracking a subject's pupil over the course of a study. This paper describes very preliminary results from using a mixture model as a processing stage. Technical issues of using a mixture model are discussed. The pixel classifications from the mixture model were fed into a naïve Bayes pupil tracker. Only low-level information is used for pupil identification. No motion tracking is performed, no belief propagation is performed, and no convolutions are computed. The algorithm is well positioned for parallel implementations. The solution surmounts several technical challenges, and initial results are unexpectedly accurate. The technique shows good promise for incorporation into a system for automatic eye-to-scene calibration.