  Eye gaze estimation has become increasingly significant in computer vision.In
this paper,we systematically study the mainstream of eye gaze estimation
methods,propose a novel methodology to estimate eye gaze points and eye gaze
directions simultaneously.First,we construct a local sharing network for
feature extraction of gaze points and gaze directions estimation,which can
reduce network computational parameters and converge quickly;Second,we propose
a Multiview Multitask Learning (MTL) framework,for gaze directions,a coplanar
constraint is proposed for the left and right eyes,for gaze points,three views
data input indirectly introduces eye position information,a cross-view pooling
module is designed, propose joint loss which handle both gaze points and gaze
directions estimation.Eventually,we collect a dataset to use of gaze
points,which have three views to exist public dataset.The experiment show our
method is state-of-the-art the current mainstream methods on two indicators of
gaze points and gaze directions.
