Head pose-free appearance-based gaze sensing via eye image synthesis

This paper addresses the problem of estimating human gaze from eye appearance under free head motion. Allowing head motion remains challenging because eye appearance changes significantly for different head poses, and thus new head poses require new training images. To avoid repetitive training, we propose to produce synthetic training images for varying head poses. First, we model pixel displacements between head-moving eye images as 1D pixel flows, and then produce such flows to synthesize new training images from the original training images captured under a fixed default head pose. Specifically, we produce all the required 1D flows by using only four additionally captured images. Our method was successfully tested with extensive experiments to demonstrate its effectiveness.