A Novel Approach to Real-time Non-intrusive Gaze Finding

A holistic approach to real-time gaze tracking is investigated by means of a well-defined neural network modelling strategy combined with robust image processing algorithms that effectively learns the gaze direction of a human user by modelling implicitly corresponding eye appearance.

Abstract

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.