# EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition

arXiv: https://arxiv.org/abs/2604.08106 · HTML: https://mlsanity.com/paper/2604.08106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Micron-BERT: BERT-based Facial Micro-Expression Recognition](https://mlsanity.com/paper/2304.03195/) (2023)
- [Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition](https://mlsanity.com/paper/1902.03634/) (2019)
- [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)
- [Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions](https://mlsanity.com/paper/1901.04656/) (2019)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [HTNet for micro-expression recognition](https://mlsanity.com/paper/2307.14637/) (2023)
- [Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition](https://mlsanity.com/paper/2112.05851/) (2021)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metrics](https://mlsanity.com/paper/1805.02397/) (2018)

## Abstract (arXiv)

Micro-expression recognition can obtain the real emotion of the individual at the current moment. Although deep learning-based methods, especially Transformer-based methods, have achieved impressive results, these methods have high computational complexity due to the large number of tokens in the multi-head self-attention. In addition, the existing micro-expression datasets are small-scale, which makes it difficult for Transformer-based models to learn effective micro-expression representations. Therefore, we propose a novel Efficient Patch tokenization, Integration and Representation framework (EPIR), which can balance high recognition performance and low computational complexity. Specifically, we first propose a dual norm shifted tokenization (DNSPT) module to learn the spatial relationship between neighboring pixels in the face region, which is implemented by a refined spatial transformation and dual norm projection. Then, we propose a token integration module to integrate partial tokens among multiple cascaded Transformer blocks, thereby reducing the number of tokens without information loss. Furthermore, we design a discriminative token extractor, which first improves the attention in the Transformer block to reduce the unnecessary focus of the attention calculation on self-tokens, and uses the dynamic token selection module (DTSM) to select key tokens, thereby capturing more discriminative micro-expression representations. We conduct extensive experiments on four popular public datasets (i.e., CASME II, SAMM, SMIC, and CAS(ME)3. The experimental results show that our method achieves significant performance gains over the state-of-the-art methods, such as 9.6% improvement on the CAS(ME)3 dataset in terms of UF1 and 4.58% improvement on the SMIC dataset in terms of UAR metric.