Deep convolutional neural networks can be trained to estimate gaze directions from eye images. However, such networks do not provide any information about the reliability of its predictions. As uncertainty estimates could enable more accurate and reliable gaze tracking applications, a method for confidence calculation was examined in this project.

This method had to be computationally efficient for the gaze tracker to function in real-time, without reducing the quality of the gaze predictions. Thus, several state-of-the-art methods were abandoned in favor of Mean-Variance Estimation, which uses an additional neural network for estimating uncertainties. This confidence network is trained based on the accuracy of the gaze rays generated by the primary network, i.e. the prediction network, for different eye images. Two datasets were used for evaluating the confidence network, including the effect of different design choices.

A main conclusion was that the uncertainty associated with a predicted gaze direction depends on more factors than just the visual appearance of the eye image. Thus, a confidence network taking only this image as input can never model the regression problem perfectly.

Despite this, the results show that the network learns useful information. In fact, its confidence estimates outperform those from an established Monte Carlo method, where the uncertainty is estimated from the spread of gaze directions from several prediction networks in an ensemble.