Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks

Recent studies on appearance based gaze estimation indicate the ability of Neural Networks to decode gaze information from facial images encompassing pose information. In this paper, we propose Gaze-Net: A capsule network capable of decoding, representing, and estimating gaze information from ocular region images. We evaluate our proposed system using two publicly available datasets, MPIIGaze (200,000+ images in the wild) and Columbia Gaze (5000+ images of users with 21 gaze directions observed at 5 camera angles/positions). Our model achieves a Mean Absolute Error (MAE) of 2.84$^\circ$ for Combined angle error estimate within dataset for MPI-IGaze dataset. Further, model achieves a MAE of 10.04$^\circ$ for across dataset gaze estimation error for Columbia gaze dataset. Through transfer learning, the error is reduced to 5.9$^\circ$. The results show this approach is promising with implications towards using commodity webcams to develop low-cost multi-user gaze tracking systems.