MFFC-Net: multi-scale feature fusion-based coordination network for gaze estimation

Abstract

Appearance-based methods with deep learning can predict the point of gaze by using a monocular camera, which requires a large sample to learn. However, existing appearance-based gaze estimation methods with deep learning mainly use face and eye images or only use a single face image, ignoring the correlation between face features and eye features In response to this issue, we propose a coordination model where face feature extraction is the gaze estimation network and eye feature extraction is the coordination network, which deeply fuses the eye-face feature relationships to perform the gaze estimation task. The model achieves good results on MPIIFaceGaze dataset and GazeCapture dataset.