A time-efficient re-calibration algorithm for improved long-term accuracy of head-worn eye trackers

Mobile gaze-based interaction has been emerging over the last two decades. Head-mounted eye trackers as well as remote systems are used to determine people's gaze (e.g., on a display). However, most state-of-the-art systems need calibration prior to usage. When using a head-mounted eye tracker, many factors (e.g., changes of eye physiology) can influence the stability of the calibration leading to less accuracy over time. Re-calibrating the system at certain time intervals is cumbersome and time-consuming. We investigate methods to minimize the time needed and optimize the process. In a user study with 16 participants, we compared partial re-calibrations with different numbers of calibration points and types of adaptation strategies. In contrast to a full calibration with nine points, the results show that a re-calibration with only three points results in 60% less time needed and achieves a similar accuracy.