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
收藏
页码：1445 / 1457
页数：12
相关论文
共 48 条
  • [1] Sun L A, Lian Z, Liu B, Tao J H., MAE-DFER: Efficient masked autoencoder for self-supervised dynamic facial expression recognition, Proceedings of the 31st ACM International Conference on Multimedia, pp. 6110-6121, (2023)
  • [2] Roy S, Etemad A., Active learning with contrastive pre-training for facial expression recognition, Proceedings of the 11th International Conference on Affective Computing and Intelligent Interaction, pp. 1-8, (2023)
  • [3] Huang J J, Li Y N, Feng J S, Wu X L, Sun X S, Ji R R., Clover: Towards a unified video-language alignment and fusion model, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14856-14866, (2023)
  • [4] Jian M W, Lam K M., Multi-view face hallucination using SVD and a mapping model, IEEE Transactions on Circuits and Systems for Video Technology, 25, 11, pp. 1761-1722, (2015)
  • [5] Liu Q H, Wu J F, Jiang Y, Bai X, Yuille A L, Bai S., InstMove: Instance motion for object-centric video segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6344-6354, (2023)
  • [6] Zhang Ying, Zhang Bing-Bing, Dong Wei, An Feng-Min, Zhang Jian-Xin, Zhang Qiang, Multi-modal video action recognition method based on language-visual contrastive learning, Acta Automatica Sinica, 50, 2, pp. 417-430, (2024)
  • [7] Lei L, Li J F, Chen T, Li S G., A novel Graph-TCN with a graph structured representation for micro-expression recognition, Proceedings of the 28th ACM International Conference on Multimedia, pp. 2237-2245, (2020)
  • [8] Lei L, Chen T, Li S G., Micro-expression recognition based on facial graph representation learning and facial action unit fusion, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1571-1580, (2021)
  • [9] Xu Feng, Zhang Jun-Ping, Facial micro-expression recognition: A survey, Acta Automatica Sinica, 43, 3, pp. 333-348, (2017)
  • [10] Li Y T, Wei J S, Liu Y., Deep learning for micr-expression recognition: A survey, IEEE Transactions on Affective Computer, 13, 4, pp. 2028-2046, (2022)
12345