Time- and space-efficient eye tracker calibration

Abstract

One of the obstacles to bring eye tracking technology to everyday human computer interactions is the time consuming calibration procedure. In this paper we investigate a novel calibration method based on smooth pursuit eye movement. The method uses linear regression to calculate the calibration mapping. The advantage is that users can perform the calibration quickly in a few seconds and only use a small calibration area to cover a large tracking area. We first describe the theoretical background on establishing a calibration mapping and discuss differences of calibration methods used. We then present a user study comparing the new regression-based method with a classical nine-point and with other pursuit-based calibrations. The results show the proposed method is fully functional, quick, and enables accurate tracking of a large area. The method has the potential to be integrated into current eye tracking systems to make them more usable in various use cases.