Ordinal information based facial expression intensity estimation for emotional interaction: a novel semi-supervised deep learning approach

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成果类型：

期刊论文

作者：

Xu, Ruyi;Han, Jiaxu;Chen, Jingying

通讯作者：

Jingying Chen

作者机构：

[Xu, Ruyi; Chen, Jingying] Cent China Normal Univ, Natl Engn Res Ctr Educ Big Data, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Chen, Jingying; Han, Jiaxu] Cent China Normal Univ, Natl Engn Res Ctr Elearning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.

[Chen, Jingying] Ningbo Yuxing Educ Technol Co Ltd, Ningbo 315200, Zhejiang, Peoples R China.

通讯机构：

[Jingying Chen] N

National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, China<&wdkj&>National Engineering Research Center for E-learning, Central China Normal University, Wuhan, China<&wdkj&>Ningbo Yuxing Educational Technology Co., Ltd, Ningbo, China

语种：

英文

关键词：

Expression intensity estimation;Siamese network;Semi-supervised learning;Ordinal regression;Social interaction analysis

期刊：

Computing

ISSN：

0010-485X

年：

2024

卷：

106

期：

页码：

1121-1138

DOI：

10.1007/s00607-022-01140-y

基金类别：

Funding was provided by National Natural Science Foundation of China (Grant No. 61977027), the Science & Technology Major Project of Hubei Province Next-Generation AI Technologies (Grant No. 2021BEA159), and the Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE, (Grant No. 30106220491).

机构署名：

本校为第一机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Emotional understanding and expression plays a critical role in social interaction. To analyze children’s emotional interaction automatically, this study focuses on developing a novel network architecture and a reliable algorithm for expression intensity estimation to measure children’s facial expression responses to emotional stimuli. The facial expression intensity variation provides temporal dynamic information of facial behavior, which is critical to interpreting the meaning of expression. In order to avoid laborious manual annotations fo...

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Ordinal information based facial expression intensity estimation for emotional interaction: a novel semi-supervised deep learning approach

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