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Human gaze is a crucial cue used in various applications such as human-robotinteraction 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 theuniqueness of eye appearance, lightning conditions, and the diversity of headpose and gaze directions. In this paper, we propose a robust CNN-based modelfor predicting gaze in unconstrained settings. We propose to regress each gazeangle separately to improve the per-angel prediction accuracy, which willenhance the overall gaze performance. In addition, we use two identical losses,one for each angle, to improve network learning and increase itsgeneralization. We evaluate our model with two popular datasets collected withunconstrained settings. Our proposed model achieves state-of-the-art accuracyof 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.

L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments