EyeNet: A Multi-Task Deep Network for Off-Axis Eye Gaze Estimation

Eye gaze estimation is a crucial component in Virtual and Mixed Reality. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation for an off-axis camera setting. The tasks include eye segmentation, IR LED glints detection, pupil and cornea center estimation. We benchmark all tasks on MagicEyes, a large and new dataset of 587 subjects with varying morphology, gender, skin-color, make-up and imaging conditions.