# Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition

arXiv: https://arxiv.org/abs/2505.16372 · HTML: https://mlsanity.com/paper/2505.16372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 72 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 17 of 17)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Transformers without Normalization](https://mlsanity.com/paper/2503.10622/) (2025)
- [RMT: Retentive Networks Meet Vision Transformers](https://mlsanity.com/paper/2309.11523/) (2023)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions](https://mlsanity.com/paper/1901.04656/) (2019)
- [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](https://mlsanity.com/paper/1911.01519/) (2019)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [MMNet: Muscle motion-guided network for micro-expression recognition](https://mlsanity.com/paper/2201.05297/) (2022)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [Micro-expression spotting: A new benchmark](https://mlsanity.com/paper/2007.12421/) (2020)
- [DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition](https://mlsanity.com/paper/2301.00985/) (2023)
- [A Unified and Interpretable Emotion Representation and Expression Generation](https://mlsanity.com/paper/2404.01243/) (2024)
- [Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning](https://mlsanity.com/paper/2408.00644/) (2024)
- [Evaluation of the Spatio-Temporal features and GAN for Micro-expression Recognition System](https://mlsanity.com/paper/1904.01748/) (2019)
- [FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse Dataset and a Decomposed Model](https://mlsanity.com/paper/2409.05396/) (2024)
- [Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition](https://mlsanity.com/paper/2406.08997/) (2024)

## Built on by (most cited first, 1 of 1)

- [AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection](https://mlsanity.com/paper/2603.08387/) (2026)

## Abstract (arXiv)

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.