Eye Tracking Using Neural Network and Mean-Shift

Abstract

In this paper, an eye tracking method is presented using a neural network (NN) and mean-shift algorithm that can accurately detect and track user's eyes under the cluttered background. In the proposed method, to deal with the rigid head motion, the facial region is first obtained using skin-color model and connected-component analysis. Thereafter the eye regions are localized using neural network (NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, which enables our method to accurately detect users' eyes even if they put on glasses. Once the eye region is localized, they are continuously and correctly tracking by mean-shift algorithm. To assess the validity of the proposed method, it is applied to the interface system using eye movement and is tested with a group of 25 users through playing a ‘aligns games.' The results show that the system process more than 30 frames/sec on PC for the 320×240 size input image and supply a user-friendly and convenient access to a computer in real-time operation.