Abstract
In order to improve the gaze estimation accuracy through using the information in the eye image more adequately, an appearance-based gaze estimation neural network EyeNet has been proposed in this article. On the one hand, by decomposing the main task into two subtasks, this network could gain more information during the training stage. On the other hand, EyeNet could extract features in the image more efficiently by adding an auxiliary task which is detecting the iris center and eye ball center of the eye. Experiments show that by using these two tricks, the error decreased from 5.42 degree to 5.16 degree.
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