Appearance-Based Gaze Estimation for Digital Signage Considering Head Pose

Digital signage often uses a large display with the camera placed on the outer frame and set with a wide angle to capture the face of the viewer. In some cases the viewer's iris is obstructed due to the gaze position of the viewer. In this study, we present an appearance-based gaze estimation method that can be used for digital signage even when the iris is not fully visible. The proposed approach uses the angle of the head and the image of the eye area as features for a neural network machine learning algorithm. Our subject experiments confirm that we achieve accurate focus-point gaze estimation.