# ARTNet for Micro-Expression Recognition

## TL;DR

This paper introduces ARTNet, a new framework that improves the recognition of subtle facial micro-expressions by amplifying motion and using transformer networks.

## Contribution

The novel ARTNet framework adjusts motion amplitude and uses transformers to better recognize individual-specific micro-expressions.

## Key findings

- ARTNet amplifies motion discrepancies to enhance expression intensity for better recognition.
- Experiments on three datasets confirm the effectiveness of the proposed method.
- The framework captures relationships between amplification features using transformer layers.

## Abstract

The field of micro-expression recognition (MER) has garnered considerable attention for its potential to reveal an individual’s genuine emotional state. However, MER remains a formidable challenge, primarily due to the subtle nature and brief duration of micro-expressions. Many approaches typically rely on optical flow to capture motion between video frames. However, these methods exhibit limited variability in expression intensity across frames, which may not be effective for all individuals due to significant differences in their micro-expressions. To address this issue, we propose a novel framework called the Action Amplification Representation and Transformer Network (ARTNet) to adjust the motion amplitude, making it easier to recognize each individual’s micro-expressions. Firstly, we amplify the motion discrepancies between frames to enhance expression intensity. Subsequently, we calculate the optical flow of these amplified frames to depict micro-expressions more prominently. Finally, we use transformer layers to capture the relationships between different amplification features. Extensive experiments conducted on three diverse datasets confirm the efficacy of our proposed method.