Abstract
In this thesis, we propose a new gaze estimation algorithm that estimates where a user looks from the eye images. The proposed gaze estimation algorithm is based on using multiple convolutional neural networks (CNN) to learn the regression networks for estimating gaze angles from eye images. The proposed algorithm can provide accurate gaze estimation for users with different head poses, since it explicitly uses the head pose information in the proposed gaze estimation framework. To achieve person independent system, we train the deep CNN regression networks with UT Multiview dataset, which contains a large number of subjects with large head pose variations. On the other hand, we estimate the head pose from the 2D face image and a generic 3D face model. It is the reason that the proposed algorithm can be widely used for appearance-based gaze estimation in practice. Our experimental results show that the proposed gaze estimation system improves the accuracy of appearance-based gaze estimation under head pose variations compared to the previous methods.