Eye-Gaze Estimation using a Deep Capsule-based Regression Network

Eye-gaze information is used in a variety of user platforms, such as driver monitoring systems and head-mounted interfaces. In order to estimate human eye-gaze, many solutions have been proposed, using different devices and techniques. However, achieving such estimation using only cheap devices like RGB cameras would enable gaze interactions on mobile devices and therefore generalise this kind of interaction. It could also enable behavior studies based on gaze and made on every day devices. We propose in this paper a new method for eye-gaze estimation using a new deep learning architecture based on the Capsule Neural Network. Capsule Networks have shown great results so far on classification tasks, but only a few works use them for regression tasks.By taking advantage of the Capsule Network architecture and its ability to reconstruct images, we are able to recreate simplified eye images and then estimate human gaze from them. Experiments are performed on two representative datasets for the task of eye-gaze estimation. Encouraging results are obtained for both the estimation and the reconstruction.