  With the immersive development in the field of augmented and virtual reality,
accurate and speedy eye-tracking is required. Facebook Research has organized a
challenge, named OpenEDS Semantic Segmentation challenge for per-pixel
segmentation of the key eye regions: the sclera, the iris, the pupil, and
everything else (background). There are two constraints set for the
participants viz MIOU and the computational complexity of the model. More
recently, researchers have achieved quite a good result using the convolutional
neural networks (CNN) in segmenting eyeregions. However, the environmental
challenges involved in this task such as low resolution, blur, unusual glint
and, illumination, off-angles, off-axis, use of glasses and different color of
iris region hinder the accuracy of segmentation. To address the challenges in
eye segmentation, the present work proposes a robust and computationally
efficient attention-based convolutional encoder-decoder network for segmenting
all the eye regions. Our model, named EyeNet, includes modified residual units
as the backbone, two types of attention blocks and multi-scale supervision for
segmenting the aforesaid four eye regions. Our proposed model achieved a total
score of 0.974(EDS Evaluation metric) on test data, which demonstrates superior
results compared to the baseline methods.

Computer Vision and Pattern Recognition