A Micro-expression Recognition Method Based on Multi-level Information Fusion Network

被引：0

作者：

Chen, Yan ^{[1
,2
]}

Wu, Le-Chen ^{[1
]}

Wang, Cong ^{[3
]}

机构：

[1] School of Computer Science, Hunan University of Technology and Business, Changsha

[2] Xiangjiang Laboratory, Changsha

[3] School of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 07期

基金：

中国国家自然科学基金;

关键词：

deep learning; graph convolutional network; Micro-expression recognition; multi-level fusion;

D O I：

10.16383/j.aas.c230641

中图分类号：

学科分类号：

摘要：

Micro-expressions are subtle and involuntary changes during emotional expression. Accurate and efficient recognition of these is crucial for the early diagnosis and treatment of mental illnesses. Most of the existing methods often neglect the connections between key facial areas in micro-expressions, making it difficult to capture the subtle changes in small sample image spaces, resulting in low recognition rates. To address this, a micro-expression recognition method is proposed based on a multi-level information fusion network. This method includes a video frame selection strategy based on frequency amplitude, which can select frames with high-intensity expressions from micro-expression videos. Additionally, this method includes a multi-level information extraction network using self-attention mechanisms and graph convolutional networks, and a fusion network that incorporates global image information, which can capture the subtle changes of facial micro-expressions from different levels to improve the recognition of specific categories. Experiments on public datasets show that our method effectively improves the accuracy and outperforms other advanced methods. © 2024 Science Press. All rights reserved.

引用

页码：1445 / 1457

页数：12

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