A New Efficient Eye Gaze Tracker for Robotic Applications

Abstract

Gaze estimation provides insight into a person's intent and engagement level, which is helpful in collaborative human-robot applications. With significant advancements in deep learning architectures, appearance-based gaze estimation has gained much attention. Appearance-based methods have shown significant improvement in gaze accuracy and, unlike traditional approaches, they function well in environments where there are no constraints. We present another convolution-based gaze estimation approach to further reduce the angular error. For estimating gaze under extreme conditions such as head variations and distances, full-face images have been shown to be efficient, so we rely on full-face and pay more attention to necessary features. With the proposed architecture, we achieve an accuracy of 3.75° on the MPIIFaceGaze dataset and 3.96° on the ETH-XGaze open-source dataset. In addition, we test eye gaze tracking in real-time robotic applications, such as attention detection, and pick-and-place.