  Gaze tracking is an important technology in many domains. Techniques such as
Convolutional Neural Networks (CNN) has allowed the invention of gaze tracking
method that relies only on commodity hardware such as the camera on a personal
computer. It has been shown that the full-face region for gaze estimation can
provide better performance than from an eye image alone. However, a problem
with using the full-face image is the heavy computation due to the larger image
size. This study tackles this problem through compression of the input
full-face image by removing redundant information using a novel learnable
pooling module. The module can be trained end-to-end by backpropagation to
learn the size of the grid in the pooling filter. The learnable pooling module
keeps the resolution of valuable regions high and vice versa. This proposed
method preserved the gaze estimation accuracy at a certain level when the image
was reduced to a smaller size.
