Twenty years of eye typing: systems and design issues
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
FreeGaze: a gaze tracking system for everyday gaze interaction
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Just blink your eyes: a head-free gaze tracking system
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Real-Time Multiple Face Detection Using Active Illumination
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGGRAPH 2004 Papers
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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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.