A Novel Approach to Real-time Non-intrusive Gaze Finding
Li-Qun Xu, David J. Machin, P. Sheppard · 1998
We investigate a holistic approach to real-time gaze tracking by means of a well-defined neural network modelling strategy combined with robust image processing algorithms. Based on captured greyscale eye images, the system effectively learns the gaze direction of a human user by modelling implicitly corresponding eye appearance -- the relative positions of the pupil, cornea, and light reflection inside the eye socket. In operation, the gaze tracker provides a fast, cheap, and flexible means finding the focus of a user's attention on any of the objects displayed on a computer screen. It works in an open-plan office environment under normal illumination without using any specialised hardware. It can be easily customised to a new user and integrated into an application system that demands an intelligent non-command interface.