Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning

• URL: http://arxiv.org/abs/2205.14643v1
• Date: Sun, 29 May 2022 12:28:10 GMT
• Title: Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning
• Authors: Yanxin Song, Jianzong Wang, Tianbo Wu, Zhangcheng Huang, Jing Xiao
• Abstract summary: We propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively.
• Score: 22.525295392858293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a challenge task. In this paper, we propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We use 3D CNN to extract RGB features and FLOW features of micro-expression sequences and fuse them, and use BERT network to extract text information in Facial Action Coding System. Through cross-modal contrastive loss, we embed attribute information in the visual network, thereby improving the representation ability of micro-expression recognition in the case of limited samples. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively. The comparative experiments show that this method has better recognition effect than other methods for micro-expression recognition.

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