  Determining the direction in which a person is looking is an important
problem in a wide range of HCI applications. In this paper we describe a highly
accurate algorithm that performs gaze estimation using an affordable and widely
available device such as Kinect. The method we propose starts by performing
accurate head pose estimation achieved by fitting a person specific morphable
model of the face to depth data. The ordinarily competing requirements of high
accuracy and high speed are met concurrently by formulating the fitting
objective function as a combination of terms which excel either in accurate or
fast fitting, and then by adaptively adjusting their relative contributions
throughout fitting. Following pose estimation, pose normalization is done by
re-rendering the fitted model as a frontal face. Finally gaze estimates are
obtained through regression from the appearance of the eyes in synthetic,
normalized images. Using EYEDIAP, the standard public dataset for the
evaluation of gaze estimation algorithms from RGB-D data, we demonstrate that
our method greatly outperforms the state of the art.
