  Development of human machine interface has become a necessity for modern day
machines to catalyze more autonomy and more efficiency. Gaze driven human
intervention is an effective and convenient option for creating an interface to
alleviate human errors. Facial landmark detection is very crucial for designing
a robust gaze detection system. Regression based methods capacitate good
spatial localization of the landmarks corresponding to different parts of the
faces. But there are still scope of improvements which have been addressed by
incorporating attention.
  In this paper, we have proposed a deep coarse-to-fine architecture called
LocalEyenet for localization of only the eye regions that can be trained
end-to-end. The model architecture, build on stacked hourglass backbone, learns
the self-attention in feature maps which aids in preserving global as well as
local spatial dependencies in face image. We have incorporated deep layer
aggregation in each hourglass to minimize the loss of attention over the depth
of architecture. Our model shows good generalization ability in cross-dataset
evaluation and in real-time localization of eyes.
