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We propose a novel neural pipeline, MSGazeNet, that learns gazerepresentations by taking advantage of the eye anatomy information through amultistream framework. Our proposed solution comprises two components, first anetwork for isolating anatomical eye regions, and a second network formultistream gaze estimation. The eye region isolation is performed with a U-Netstyle network which we train using a synthetic dataset that contains eye regionmasks for the visible eyeball and the iris region. The synthetic dataset usedin this stage is a new dataset consisting of 60,000 eye images, which we createusing an eye-gaze simulator, UnityEyes. Successive to training, the eye regionisolation network is then transferred to the real domain for generating masksfor the real-world eye images. In order to successfully make the transfer, weexploit domain randomization in the training process, which allows for thesynthetic images to benefit from a larger variance with the help ofaugmentations that resemble artifacts. The generated eye region masks alongwith the raw eye images are then used together as a multistream input to ourgaze estimation network. We evaluate our framework on three benchmark gazeestimation datasets, MPIIGaze, Eyediap, and UTMultiview, where we set a newstate-of-the-art on Eyediap and UTMultiview datasets by obtaining a performancegain of 7.57% and 1.85% respectively, while achieving competitive performanceon MPIIGaze. We also study the robustness of our method with respect to thenoise in the data and demonstrate that our model is less sensitive to noisydata. Lastly, we perform a variety of experiments including ablation studies toevaluate the contribution of different components and design choices in oursolution.

Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning