Self-training Pupil Detection Based on Mouse Click Calibration for Eye-Tracking Under Low Resolution

Pupil detection is an indispensable step in eye-tracking, however, there are lots of difficulties in locating the pupil under low-resolution environments. This study proposes a self-training method of pupil detection for eye-tracking under low resolution. The system is applied to a typical application scenario as using a personal computer, a webcam with a resolution of 640 ×480 is used in our study. We first generate an initial pupil pattern by verifying the color intensity of the eye regions and estimate the gaze according to the matched pupil position in the further frames. After that, we utilize the computer users' actions of mouse clicks to calibrate the estimation result and annotate the position of the pupil to train the weight map of the pupil pattern. The experimental result shows that the proposed method could minimize the effect of bright spots, moreover, the precision of pupil detection and eye-tracking is increased. The proposed method will be potentially used in light-weight applications of human-computer interaction.