Author:
Zhou Jinchao,Li Guoan,Shi Feng,Guo Xiaoyan,Wan Pengfei,Wang Miao
Abstract
AbstractIn 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.
Funder
National Natural Science Foundation of China
the Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Visual Arts and Performing Arts,Medicine (miscellaneous),Computer Science (miscellaneous),Software