Yante Li and Guoying Zhao. 2021. Intra- and Inter-Contrastive Learning for Micro-expression Action Unit Detection. Proceedings of the 2021 International Conference on Multimodal Interaction. Association for Computing Machinery, New York, NY, USA, 702–706. https://doi.org/10.1145/3462244.3479956
Intra- and inter-contrastive learning for micro-expression action unit detection
|Author:||Li, Yante1; Zhao, Guoying1|
1University of Oulu
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021121060120
Association for Computing Machinery,
|Publish Date:|| 2021-12-10
Encoding facial expressions via Action Units (AUs) has been found effective for resolving the ambiguity issue among different expressions. In the literature, AU detection has extensive researches in macro-expressions. However, there is limited research about AU analysis for micro-expressions (MEs). Micro-expression Action Unit (MEAU) detection becomes a challenging problem because of the subtle facial motion. To alleviate this problem, in this paper, we study the contrastive learning for modeling subtle AUs and propose a novel MEAU detection method by learning the intra- and inter-contrastive information among MEs. Through the intra-contrastive learning module, the difference between the onset and apex frames is enlarged and utilized to obtain the discriminative representation for low-intensity AU detection. In addition, considering the subtle difference between MEAUs, the inter-contrastive learning is designed to automatically explore and enlarge the difference between different AUs to enhance the MEAU detection robustness. Intensive experiments on two widely used ME databases have demonstrated the effectiveness and generalization ability of our proposed method.
|Pages:||702 - 706|
ICMI '21: Proceedings of the 2021 International Conference on Multimodal Interaction
ACM International Conference on Multimodal Interaction
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work was supported by Infotech Oulu, National Natural Science Foundation of China (Grant No: 61772419), Ministry of Education and Culture of Finland for AI forum project, and Academy of Finland for ICT 2023 project (grant 328115).
|Academy of Finland Grant Number:||
328115 (Academy of Finland Funding decision)
© 2021 Association for Computing Machinery. The final authenticated version is available online at https://doi.org/10.1145/3462244.3479956.