  Gaze-tracking is a novel way of interacting with computers which allows new
scenarios, such as enabling people with motor-neuron disabilities to control
their computers or doctors to interact with patient information without
touching screen or keyboard. Further, there are emerging applications of
gaze-tracking in interactive gaming, user experience research, human attention
analysis and behavioral studies. Accurate estimation of the gaze may involve
accounting for head-pose, head-position, eye rotation, distance from the object
as well as operating conditions such as illumination, occlusion, background
noise and various biological aspects of the user. Commercially available
gaze-trackers utilize specialized sensor assemblies that usually consist of an
infrared light source and camera. There are several challenges in the universal
proliferation of gaze-tracking as accessibility technologies, specifically its
affordability, reliability, and ease-of-use. In this paper, we try to address
these challenges through the development of a hardware-agnostic gaze-tracker.
We present a deep neural network architecture as an appearance-based method for
constrained gaze-tracking that utilizes facial imagery captured on an ordinary
RGB camera ubiquitous in all modern computing devices. Our system achieved an
error of 1.8073cm on GazeCapture dataset without any calibration or device
specific fine-tuning. This research shows promise that one day soon any
computer, tablet, or phone will be controllable using just your eyes due to the
prediction capabilities of deep neutral networks.
