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.
Highlights
A wide variety of gaze tracking systems are available commercially, each tailored for a specific set of applications
Video-based eye-tracking is based on the principle that when near infrared (NIR) light is shone onto the eyes, it is reflected off the different structures in the eye to create four Purkinje reflections (Crane and Steele, 1985)
Tracking a person's gaze with a video-based system involves a number of steps. These steps can be loosely grouped into two sets, namely those involved with the detection of the eyes and eye features in the video frames, and those which map the detected features to gaze coordinates or Point of Regard (PoR) on the stimulus
Summary
A wide variety of gaze tracking systems are available commercially, each tailored for a specific set of applications. The vector difference between the pupil centre and the first Purkinje image (PI) ( known as the glint or corneal reflection (CR)), is tracked. Tracking a person's gaze with a video-based system involves a number of steps. These steps can be loosely grouped into two sets, namely those involved with the detection of the eyes and eye features (e.g. pupil and glint centres) in the video frames, and those which map the detected features to gaze coordinates or Point of Regard (PoR) on the stimulus. For purposes of this paper, it is assumed that the location of features in the eye video is known and the focus is on the challenge to use these as input to determine a person's Point of Regard
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