Deterministic and stochastic methods for gaze tracking in real-time

Abstract

Psychological evidence demonstrates how eye gaze analysis is requested for human computer interaction endowed with emotion recognition capabilities. The existing proposals analyse eyelid and iris motion by using colour information and edge detectors, but eye movements are quite fast and difficult for precise and robust tracking. Instead, we propose to reduce the dimensionality of the image-data by using multi-Gaussian modelling and transition estimations by applying partial differences. The tracking system can handle illumination changes, low-image resolution and occlusions while estimating eyelid and iris movements as continuous variables. Therefore, this is an accurate and robust tracking system for eyelids and irises in 3D for standard image quality.