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

作　　者：Jinchao Zhou Guoan Li Feng Shi Xiaoyan Guo Pengfei Wan Miao Wang 

机构地区：[1]State Key Laboratory of Virtual Reality Technology and Systems,Beihang University,Beijing 100191,China [2]Kuaishou Technology,Beijing 100085,China

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2023年第1期97-108,共12页工医艺的可视计算（英文）

基　　金：the National Natural Science Foundation of China,No.61932003;and the Fundamental Research Funds for the Central Universities.

摘　　要：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 perfor-mances are improved.The experiments demonstrate that the proposed method outperforms existing gaze-estima-tion methods on the GazeCapture and MPIIFaceGaze datasets.

关 键 词：Computer vision Gaze estimation Metric learning ATTENTION Multi-task learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]