Easy post-hoc spatial recalibration of eye tracking data

Abstract

The gaze locations reported by eye trackers often contain error resulting from a variety of sources. Such error is of increasing concern to eye tracking researchers, and several techniques have been introduced to clean up the error. These methods, however, either compensate only for error caused by a particular source (such as pupil dilation) or require the error to be somewhat constant across space and time. This paper introduces a method that is applicable to error generated from a variety of sources and that is resilient to the change in error across the display. A study shows that, at least in some cases, although the change in error across the display appears to be random it in fact follows a consistent pattern which can be modeled using quadratic equations. The parameters of these equations can be estimated using linear regression on the error vectors between recorded fixations and possible target locations. The resulting equations can then be used to clean up the error. This regression-based approach is much easier to apply than some of the previously published methods. The method is applied to the data of a visual search experiment, and the results show that the regression-based error correction works very well.