Identifying users based on their eye tracker calibration data

The purpose of the paper is to test the possibility of identifying people based on the input they provide to an eye tracker during the calibration process. The most popular eye trackers require the calibration before their first usage. The calibration model that is built can recalculate the subsequent eye tracker’s output to genuine gaze points. It is well known that the model is idiosyncratic (individual for the person). The calibration should be repeated every time the person uses the eye tracker. However, there is evidence that the models created for the same persons may be reused by them (but obviously with some loss of accuracy). The general idea investigated in this paper is that if we take an uncalibrated eye tracker’s output and compare it with the genuine gaze points, the errors will be repeatable for the same person. We tested this idea using three datasets with an eye tracker signal recorded for 52 users. The results are promising as the accuracy of identification (1 of N) for the datasets varied from 49% to 71%.