Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition

When emotions are repressed, an individual's true feelings may be revealed through micro-expressions. Consequently, micro-expressions are regarded as a genuine source of insight into an individual's authentic emotions. However, the transient and highly localised nature of micro-expressions poses a significant challenge to their accurate recognition, with the accuracy rate of micro-expression recognition being as low as 50%, even for professionals. In order to address these challenges, it is necessary to explore the field of dynamic micro expression recognition (DMER) using multimodal fusion techniques, with special attention to the diverse fusion of temporal and spatial modal features. In this paper, we propose a novel Temporal and Spatial feature Fusion framework for DMER (TSFmicro). This framework integrates a Retention Network (RetNet) and a transformer-based DMER network, with the objective of efficient micro-expression recognition through the capture and fusion of temporal and spatial relations. Meanwhile, we propose a novel parallel time-space fusion method from the perspective of modal fusion, which fuses spatio-temporal information in high-dimensional feature space, resulting in complementary "where-how" relationships at the semantic level and providing richer semantic information for the model. The experimental results demonstrate the superior performance of the TSFmicro method in comparison to other contemporary state-of-the-art methods. This is evidenced by its effectiveness on three well-recognised micro-expression datasets.

A Visual Self-attention Mechanism Facial Expression Recognition Network beyond Convnext

Facial expression recognition is an important research direction in the field of artificial intelligence. Although new breakthroughs have been made in recent years, the uneven distribution of datasets and the similarity between different categories of facial expressions, as well as the differences within the same category among different subjects, remain challenges. This paper proposes a visual facial expression signal feature processing network based on truncated ConvNeXt approach(Conv-cut), to improve the accuracy of FER under challenging conditions. The network uses a truncated ConvNeXt-Base as the feature extractor, and then we designed a Detail Extraction Block to extract detailed features, and introduced a Self-Attention mechanism to enable the network to learn the extracted features more effectively. To evaluate the proposed Conv-cut approach, we conducted experiments on the RAF-DB and FERPlus datasets, and the results show that our model has achieved state-of-the-art performance. Our code could be accessed at Github.