Gaze Detection and Prediction Using Data from Infrared Cameras

Abstract

Knowing the point of gaze on a screen can benefit a variety of applications and improve user experiences. Some electronic devices with infrared cameras can generate 3D point cloud for user identification. We propose a paradigm to use 3D point cloud and eye images for gaze detection and prediction. Our method fuses 3D point cloud with eye images by image registration methods. We develop a cost function to detect saggital plane from point cloud data, and reconstruct a symmetric face by saggital plane. Symmetric face data increase the accuracy of gaze detection. We use long-short term memory models to track head and eye movement, and predict next point of gaze. Our method utilizes the existing hardware setup and provides options to improve user experiences.