Idiosyncratic repeatability of calibration errors during eye tracker calibration

Dynamic development of high quality cameras and algorithms processing eye movement signals entails growing interests in using them in various areas of human-computer interaction. Determining subjects which user is looking at or controlling the operation of computer processes can serve as examples of these areas. However, making eye movement signal valuable requires some preparatory steps to be taken. They belong to a process called calibration aiming at creating a model for mapping output delivered by an eye tracker to user's gaze points. The quality of such model is assessed based on a calibration error defined as a difference between accurate data and this obtained from a model. The goal of the research presented in the paper was to analyse to what extent the calibration error depends on the specific participant's features - it is repeatable - or to what extent it may be avoided during the recalibration. Additionally an influence of two calibration method a polynomial and an artificial neural network (ANN) on the final results were studied as well.