  Learning-based methods are believed to work well for unconstrained gaze
estimation, i.e. gaze estimation from a monocular RGB camera without
assumptions regarding user, environment, or camera. However, current gaze
datasets were collected under laboratory conditions and methods were not
evaluated across multiple datasets. Our work makes three contributions towards
addressing these limitations. First, we present the MPIIGaze that contains
213,659 full face images and corresponding ground-truth gaze positions
collected from 15 users during everyday laptop use over several months. An
experience sampling approach ensured continuous gaze and head poses and
realistic variation in eye appearance and illumination. To facilitate
cross-dataset evaluations, 37,667 images were manually annotated with eye
corners, mouth corners, and pupil centres. Second, we present an extensive
evaluation of state-of-the-art gaze estimation methods on three current
datasets, including MPIIGaze. We study key challenges including target gaze
range, illumination conditions, and facial appearance variation. We show that
image resolution and the use of both eyes affect gaze estimation performance
while head pose and pupil centre information are less informative. Finally, we
propose GazeNet, the first deep appearance-based gaze estimation method.
GazeNet improves the state of the art by 22% percent (from a mean error of 13.9
degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.
