论文标题
基于属性信息嵌入和跨模式对比学习的微表达识别
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning
论文作者
论文摘要
面部微表达识别最近引起了很多关注。微表达具有短持续时间和低强度的特征，并且很难训练具有有限数量现有微表达的高性能分类器。因此，识别微表达是一项挑战任务。在本文中，我们提出了一种基于属性信息嵌入和跨模式对比学习的微表达识别方法。我们使用3D CNN来提取Micro-Expression序列的RGB功能和流动特征并融合它们，并使用BERT网络在面部动作编码系统中提取文本信息。通过跨模式的对比损失，我们将属性信息嵌入了视觉网络，从而提高了在有限的样本的情况下，微表达识别的表示能力。我们在CASME II和MMEW数据库中进行了广泛的实验，精度分别为77.82％和71.04％。比较实验表明，该方法比微表达识别的其他方法具有更好的识别效果。
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.
