Neural networks for optical vector and eye ball parameter estimation

In this work we evaluate neural networks, support vector machines and decision trees for the regression of the center of the eyeball and the optical vector based on the pupil ellipse. In the evaluation we analyze single ellipses as well as window-based approaches as input. Comparisons are made regarding accuracy and runtime. The evaluation gives an overview of the general expected accuracy with different models and amounts of input ellipses. A simulator was implemented for the generation of the training and evaluation data. For a visual evaluation and to push the state of the art in optical vector estimation, the best model was applied to real data. This real data came from public data sets in which the ellipse is already annotated by an algorithm. The optical vectors on real data and the generator are made publicly available. Link to the generator and models.