# Micro-Expression Recognition via Fine-Grained Dynamic Perception

arXiv: https://arxiv.org/abs/2509.06015 · HTML: https://mlsanity.com/paper/2509.06015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Bottleneck Transformers for Visual Recognition](https://mlsanity.com/paper/2101.11605/) (2021)
- [CMT: Convolutional Neural Networks Meet Vision Transformers](https://mlsanity.com/paper/2107.06263/) (2021)
- [Incorporating Convolution Designs into Visual Transformers](https://mlsanity.com/paper/2103.11816/) (2021)
- [Pointwise Convolutional Neural Networks](https://mlsanity.com/paper/1712.05245/) (2017)
- [JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention](https://mlsanity.com/paper/2003.08834/) (2020)
- [Facial Action Unit Detection Using Attention and Relation Learning](https://mlsanity.com/paper/1808.03457/) (2018)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis](https://mlsanity.com/paper/2011.10185/) (2020)
- [Less is More: Micro-expression Recognition from Video using Apex Frame](https://mlsanity.com/paper/1606.01721/) (2016)
- [Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition](https://mlsanity.com/paper/2006.09674/) (2020)
- [Rank Pooling for Action Recognition](https://mlsanity.com/paper/1512.01848/) (2015)
- [Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition](https://mlsanity.com/paper/1805.08417/) (2018)
- [LEARNet Dynamic Imaging Network for Micro Expression Recognition](https://mlsanity.com/paper/1904.09410/) (2019)
- [Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks](https://mlsanity.com/paper/1904.01390/) (2019)
- [Generalized Rank Pooling for Activity Recognition](https://mlsanity.com/paper/1704.02112/) (2017)
- [Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis & Application](https://mlsanity.com/paper/1601.04805/) (2016)
- [Facial Action Unit Detection via Adaptive Attention and Relation](https://mlsanity.com/paper/2001.01168/) (2020)
- [Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample](https://mlsanity.com/paper/2410.01251/) (2024)

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

- [STAG: Spatio-temporal Evolving Structural Representation of Action Units for Micro-expression Recognition](https://mlsanity.com/paper/2606.28083/) (2026)

## Abstract (arXiv)

Facial micro-expression recognition (MER) is a challenging task, due to the transience, subtlety, and dynamics of micro-expressions (MEs). Most existing methods resort to hand-crafted features or deep networks, in which the former often additionally requires key frames, and the latter suffers from small-scale and low-diversity training data. In this paper, we develop a novel fine-grained dynamic perception (FDP) framework for MER. We propose to rank frame-level features of a sequence of raw frames in chronological order, in which the rank process encodes the dynamic information of both ME appearances and motions. Specifically, a novel local-global feature-aware transformer is proposed for frame representation learning. A rank scorer is further adopted to calculate rank scores of each frame-level feature. Afterwards, the rank features from rank scorer are pooled in temporal dimension to capture dynamic representation. Finally, the dynamic representation is shared by a MER module and a dynamic image construction module, in which the former predicts the ME category, and the latter uses an encoder-decoder structure to construct the dynamic image. The design of dynamic image construction task is beneficial for capturing facial subtle actions associated with MEs and alleviating the data scarcity issue. Extensive experiments show that our method (i) significantly outperforms the state-of-the-art MER methods, and (ii) works well for dynamic image construction. Particularly, our FDP improves by 4.05%, 2.50%, 7.71%, and 2.11% over the previous best results in terms of F1-score on the CASME II, SAMM, CAS(ME)^2, and CAS(ME)^3 datasets, respectively. The code is available at https://github.com/CYF-cuber/FDP.