  From scientific research to commercial applications, eye tracking is an
important tool across many domains. Despite its range of applications, eye
tracking has yet to become a pervasive technology. We believe that we can put
the power of eye tracking in everyone's palm by building eye tracking software
that works on commodity hardware such as mobile phones and tablets, without the
need for additional sensors or devices. We tackle this problem by introducing
GazeCapture, the first large-scale dataset for eye tracking, containing data
from over 1450 people consisting of almost 2.5M frames. Using GazeCapture, we
train iTracker, a convolutional neural network for eye tracking, which achieves
a significant reduction in error over previous approaches while running in real
time (10-15fps) on a modern mobile device. Our model achieves a prediction
error of 1.71cm and 2.53cm without calibration on mobile phones and tablets
respectively. With calibration, this is reduced to 1.34cm and 2.12cm. Further,
we demonstrate that the features learned by iTracker generalize well to other
datasets, achieving state-of-the-art results. The code, data, and models are
available at http://gazecapture.csail.mit.edu.
