Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction, such as driver fatigue detection, eye gaze replacing the hand operating mouse, eye typing instead of manually depressing keys as a virtual keyboard, eye gaze correction for video conferencing, interactive assistant application for disabled users, etc. However, due to the eye motion be the high nonlinearity, the obstacles of robustness of external interference and accuracy of eye tracking, these tend to significantly limit their scope of application. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, and overcome the modeling of nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify prior covariance matrix to improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions to eye tracking. The last experimental results show validity of our method for eye tracking under realistic conditions.