# Adaptive Fusion Network with Temporal-Ranked and Motion-Intensity Dynamic Images for Micro-expression Recognition

arXiv: https://arxiv.org/abs/2510.09730 · HTML: https://mlsanity.com/paper/2510.09730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Rank Pooling for Action Recognition](https://mlsanity.com/paper/1512.01848/) (2015)
- [MMNet: Muscle motion-guided network for micro-expression recognition](https://mlsanity.com/paper/2201.05297/) (2022)
- [3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information](https://mlsanity.com/paper/2006.07743/) (2020)
- [MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution](https://mlsanity.com/paper/2506.14511/) (2025)
- [AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition](https://mlsanity.com/paper/2104.07569/) (2021)
- [Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition](https://mlsanity.com/paper/2005.07991/) (2020)

## Abstract (arXiv)

Micro-expressions (MEs) are subtle, transient facial changes with very low intensity, almost imperceptible to the naked eye, yet they reveal a person genuine emotion. They are of great value in lie detection, behavioral analysis, and psychological assessment. This paper proposes a novel MER method with two main contributions. First, we propose two complementary representations - Temporal-ranked dynamic image, which emphasizes temporal progression, and Motion-intensity dynamic image, which highlights subtle motions through a frame reordering mechanism incorporating motion intensity. Second, we propose an Adaptive fusion network, which automatically learns to optimally integrate these two representations, thereby enhancing discriminative ME features while suppressing noise. Experiments on three benchmark datasets (CASME-II, SAMM and MMEW) demonstrate the superiority of the proposed method. Specifically, AFN achieves 93.95 Accuracy and 0.897 UF1 on CASME-II, setting a new state-of-the-art benchmark. On SAMM, the method attains 82.47 Accuracy and 0.665 UF1, demonstrating more balanced recognition across classes. On MMEW, the model achieves 76.00 Accuracy, further confirming its generalization ability. The obtained results show that both the input and the proposed architecture play important roles in improving the performance of MER. Moreover, they provide a solid foundation for further research and practical applications in the fields of affective computing, lie detection, and human-computer interaction.