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

TL;DR

EPIR is a novel framework that enhances micro-expression recognition by balancing high accuracy with low computational cost through innovative tokenization and integration techniques.

Contribution

The paper introduces a dual norm shifted tokenization module, a token integration module, and a discriminative token extractor with dynamic token selection, improving Transformer efficiency.

Findings

Achieves 9.6% UF1 improvement on CAS(ME)$^3$ dataset.

Attains 4.58% UAR improvement on SMIC dataset.

Outperforms state-of-the-art methods on four public datasets.

Abstract

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.