Research

In this work, we investigate the hypothesis that adding 3D information of the periocular region to an end-to-end gaze-estimation network can improve gaze-estimation accuracy in the presence of slippage, which occurs quite commonly for head-mounted AR/VR devices. To this end, using UnityEyes we generate a simulated dataset with RGB and depth-maps of the eye with varying camera placement to simulate slippage artifacts. We generate different noise profiles for the depth-maps to simulate depth sensor noise artifacts. Using this data, we investigate the effects of different fusion techniques for combining image and depth information for gaze estimation. Our experiments show that under an attention-based fusion scheme, 3D information can significantly improve gaze-estimation and compensates well for slippage induced variability. Our finding supports augmenting 2D cameras with depth-sensors for the development of robust end-to-end appearance based gaze-estimation systems.