EM-Gaze: eye context correlation and metric learning for gaze estimation

Abstract

In recent years, deep learning techniques have been used to estimate gaze—a significant task in computer vision and human-computer interaction. Previous studies have made significant achievements in predicting 2D or 3D gazes from monocular face images. This study presents a deep neural network for 2D gaze estimation on mobile devices. It achieves state-of-the-art 2D gaze point regression error, while significantly improving gaze classification error on quadrant divisions of the display. To this end, an efficient attention-based module that correlates and fuses the left and right eye contextual features is first proposed to improve gaze point regression performance. Subsequently, through a unified perspective for gaze estimation, metric learning for gaze classification on quadrant divisions is incorporated as additional supervision. Consequently, both gaze point regression and quadrant classification performances are improved. The experiments demonstrate that the proposed method outperforms existing gaze-estimation methods on the GazeCapture and MPIIFaceGaze datasets.