@inproceedings{a8a109fd24b04e2da6e9770f40195d5c,
title = "An Interpretable Deep Bayesian Model for Facial Micro-Expression Recognition",
abstract = "The Bayesian network is a powerful model for uncertain causal inference, but is limited to handle numerical data. In order to apply its excellent bidirectional inference ability to the image domain, this paper proposes an interpretable deep Bayesian model, which is based on deep learning technology to conduct semantic segmentation of facial micro-expressions and then extract features to construct the feature Bayesian network to analyze and infer causal relationships. Experiments show that the proposed model enables Bayesian networks to analyze image information, and enhances the interpretability of micro-expression recognition compared with deep learning models.",
keywords = "Bayesian network, deep learning, interpretable, micro-expression recognition",
author = "Chenfeng Wang and Xiaoguang Gao and Xinyu Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th International Conference on Control and Robotics Engineering, ICCRE 2023 ; Conference date: 21-04-2023 Through 23-04-2023",
year = "2023",
doi = "10.1109/ICCRE57112.2023.10155596",
language = "英语",
series = "2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "91--94",
booktitle = "2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023",
}