Robust Eye and Pupil Detection Method for Gaze Tracking

Abstract

Robust and accurate pupil detection is a prerequisite for gaze detection. Hence, we propose a new eye/pupil detection method for gaze detection on a large display. The novelty of our research can be summarized by the following four points. First, in order to overcome the performance limitations of conventional methods of eye detection, such as adaptive boosting (Adaboost) and continuously adaptive mean shift (CAMShift) algorithms, we propose adaptive selection of the Adaboost and CAMShift methods. Second, this adaptive selection is based on two parameters: pixel differences in successive images and matching values determined by CAMShift. Third, a support vector machine (SVM)-based classifier is used with these two parameters as the input, which improves the eye detection performance. Fourth, the center of the pupil within the detected eye region is accurately located by means of circular edge detection, binarization and calculation of the geometric center. The experimental results show that the proposed method can detect the center of the pupil at a speed of approximately 19.4 frames/s with an RMS error of approximately 5.75 pixels, which is superior to the performance of conventional detection methods.