A Gaze Estimation Method Based on Binocular Cameras

Abstract

In recent years, multi-stream gaze estimation methods have become mainstream, which estimate gaze point by eye picture or combine with facial appearance, have achieved considerable accuracy. However, these methods based on a single camera fail to obtain accurate eye spatial position information. To address this issue, we propose a multi-stream gaze estimation model that incorporates spatial position information. We acquire eye spatial position information using a stereo camera and fuse eye image features with eye spatial position information using a ResNet network with a fused attention mechanism. Additionally, we perform calibration of eye image features using the computed eye spatial position information. Our model demonstrates superior performance on our experimental dataset.