Human Computer Interaction

Neural networks for optical vector and eye ball parameter estimation

In this work we evaluate neural networks, support vector machinesand decision trees for the regression of the center of the eyeballand the optical vector based on the pupil ellipse. In the evaluationwe analyze single ellipses as well as window-based approaches asinput. Comparisons are made regarding accuracy and runtime. Theevaluation gives an overview of the general expected accuracy withdifferent models and amounts of input ellipses. A simulator wasimplemented for the generation of the training and evaluation data.For a visual evaluation and to push the state of the art in opticalvector estimation, the best model was applied to real data. Thisreal data came from public data sets in which the ellipse is alreadyannotated by an algorithm. The optical vectors on real data and thegenerator are made publicly available.

In this work we evaluate neural networks, support vector machinesand decision trees for the regression of the center of the eyeballand the optical vector based on the pupil ellipse. In the evaluationwe analyze single ellipses as well as window-based approaches asinput. Comparisons are made regarding accuracy and runtime. Theevaluation gives an overview of the general expected accuracy withdifferent models and amounts of input ellipses. A simulator wasimplemented for the generation of the training and evaluation data.For a visual evaluation and to push the state of the art in opticalvector estimation, the best model was applied to real data. Thisreal data came from public data sets in which the ellipse is alreadyannotated by an algorithm. The optical vectors on real data and thegenerator are made publicly available.

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