An Interpretable Deep Bayesian Model for Facial Micro-Expression Recognition

Chenfeng Wang, Xiaoguang Gao, Xinyu Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

5 引用 (Scopus)

摘要

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.

源语言英语
主期刊名2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023
出版商Institute of Electrical and Electronics Engineers Inc.
91-94
页数4
ISBN(电子版)9798350345650
DOI
出版状态已出版 - 2023
活动8th International Conference on Control and Robotics Engineering, ICCRE 2023 - Niigata, 日本
期限: 21 4月 202323 4月 2023

出版系列

姓名2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023

会议

会议8th International Conference on Control and Robotics Engineering, ICCRE 2023
国家/地区日本
Niigata
时期21/04/2323/04/23

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