LAUSR

Facial occlusion and different appearances of both eyes in natural scenes can affect the accuracy of gaze estimation based on appearance. Therefore, this paper proposes a gaze estimation model based on cooperative network: CI-Net, including a consistency estimation network (C-Net) and inconsistency estimation network (I-Net). C-Net is used to estimate the Main gaze of the true gaze, and an attention mechanism is added to adaptively assign the weight between eyes and face features. The I-Net is used to estimate the Residual residuals based on true gaze. In addition, Cross attention module is designed in this paper, through which I-Net can selectively obtain information from C-Net, to obtain more accurate eyes directions. The experimental results in this paper show that the CI-Net gain lower angle errors than the current mainstream CNN methods under the condition of different appearance of both eyes and facial occlusion.