  Recently, many multi-stream gaze estimation methods have been proposed. They
estimate gaze from eye and face appearances and achieve reasonable accuracy.
However, most of the methods simply concatenate the features extracted from eye
and face appearance. The feature fusion process has been ignored. In this
paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which
performs gaze tracking task in mobile tablets. We stack two-eye feature maps
and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features
according to their similarity on appearance. Meanwhile, we also propose
Adaptive Group Normalization to recalibrate eye features with the guidance of
facial feature. Extensive experiments on both GazeCapture and MPIIFaceGaze
datasets demonstrate consistently superior performance of the proposed method.
