Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning
This work addresses the challenge of recognizing subtle facial expressions with limited data, which is important for applications like psychology and security, though it appears incremental as it builds on existing techniques like 3D CNN and BERT.
The paper tackles micro-expression recognition by proposing a method that embeds attribute information and uses cross-modal contrastive learning to improve representation with limited samples, achieving accuracies of 77.82% on CASME II and 71.04% on MMEW databases.
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.