GCANet: Geometry cues-aware facial expression recognition based on graph convolutional networks

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Wang, Shutong;Zhao, Anran;Lai, Chenghang;Zhang, Qi;Li, Duantengchuan;...

通讯作者：

Zhang, Q

作者机构：

[Wang, Xiaoguang; Wang, Shutong] Wuhan Univ, Sch Informat Management, Wuhan 430072, Peoples R China.

[Wang, Shutong] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.

[Zhao, Anran] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.

[Lai, Chenghang] Fudan Univ, Sch Comp Sci, Shanghai 200438, Peoples R China.

[Zhang, Qi; Zhang, Q] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.

通讯机构：

[Zhang, Q ] C

Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.

语种：

英文

关键词：

Facial expression recognition;Graph convolutional network;Geometry cue;Uncertainty;Emotion label distribution learning

期刊：

Journal of King Saud University - Computer and Information Sciences

ISSN：

1319-1578

年：

2023

卷：

期：

页码：

101605

DOI：

10.1016/j.jksuci.2023.101605

基金类别：

National Office for Philosophy and Social Sciences Chinese National Funding of Social Sciences#&#&#21&ZD334 Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China#&#&#22YJC890005

机构署名：

本校为通讯机构

院系归属：

信息管理学院

国家数字化学习工程技术研究中心

摘要：

Facial expression recognition (FER) task in the wild is challenging due to some uncertainties, such as the ambiguity of facial expressions, subjective annotations, and low-quality facial images. A novel model for FER in-the-wild datasets is proposed in this study to solve these uncertainties. The overview of the proposed method is as follows. First, the facial images are grouped into high and low uncertainties by the pre-trained network. The graph convolutional network (GCN) framework is then used for the facial images with low uncertainty to obtain geometry cues, including the relationship am...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

GCANet: Geometry cues-aware facial expression recognition based on graph convolutional networks

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问