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.

通过属性信息嵌入和跨模态对比学习，基于微表情序列的 RGB 和 FLOW 特征提取和 BERT 网络提取文本信息，实现面部微表情识别，并在 CASME II 和 MMEW 数据库上进行了广泛实验，准确率分别为 77.82% 和 71.04%。与其他方法相比，该方法具有更好的微表情识别效果。