Mapping the Pupil-Glint Vector to Gaze Coordinates in a Simple Video-Based Eye Tracker

Abstract

In a video-based eye tracker, the normalized pupil-glint vector changes as the eyes move. Using an appropriate model, the pupil-glint vector can be mapped to gaze coordinates. Using a simple hardware configuration with one camera and one infrared source, several mapping functions – some from literature and some derived here – were compared with one another with respect to the accuracy that could be achieved. The study served to confirm the results of a previous study with another data set and to expand on the possibilities that are considered from the previous study. The data of various participants was examined for trends which led to derivation of a mapping model that proved to be more accurate than all but one model from literature. It was also shown that the best calibration configuration for this hardware setup is one that contains fourteen targets while taking about 20 seconds for the procedure to be completed.

Keywords

• eye tracking
• gaze estimation
• polynomial models
• calibration targets
• accuracy
• validation