  Human gaze is a crucial cue used in various applications such as human-robot
interaction and virtual reality. Recently, convolution neural network (CNN)
approaches have made notable progress in predicting gaze direction. However,
estimating gaze in-the-wild is still a challenging problem due to the
uniqueness of eye appearance, lightning conditions, and the diversity of head
pose and gaze directions. In this paper, we propose a robust CNN-based model
for predicting gaze in unconstrained settings. We propose to regress each gaze
angle separately to improve the per-angel prediction accuracy, which will
enhance the overall gaze performance. In addition, we use two identical losses,
one for each angle, to improve network learning and increase its
generalization. We evaluate our model with two popular datasets collected with
unconstrained settings. Our proposed model achieves state-of-the-art accuracy
of 3.92{\deg} and 10.41{\deg} on MPIIGaze and Gaze360 datasets, respectively.
We make our code open source at https://github.com/Ahmednull/L2CS-Net.
