Hierarchical Space-Time Attention for Micro-Expression Recognition

TL;DR

This paper introduces Hierarchical Space-Time Attention (HSTA), a novel model for micro-expression recognition that captures subtle facial movements by integrating space-time relationships and crossmodal data fusion, leading to improved accuracy.

Contribution

The paper proposes a hierarchical attention framework combining Unimodal and Crossmodal Space-Time Attention to better model facial cues in micro-expression videos, addressing limitations of previous methods.

Findings

Achieves about 3% improvement on CASME3 dataset in seven-category classification.

Effectively models temporal and spatial facial cues.

Outperforms recent methods on four benchmark datasets.

Abstract

Micro-expression recognition (MER) aims to recognize the short and subtle facial movements from the Micro-expression (ME) video clips, which reveal real emotions. Recent MER methods mostly only utilize special frames from ME video clips or extract optical flow from these special frames. However, they neglect the relationship between movements and space-time, while facial cues are hidden within these relationships. To solve this issue, we propose the Hierarchical Space-Time Attention (HSTA). Specifically, we first process ME video frames and special frames or data parallelly by our cascaded Unimodal Space-Time Attention (USTA) to establish connections between subtle facial movements and specific facial areas. Then, we design Crossmodal Space-Time Attention (CSTA) to achieve a higher-quality fusion for crossmodal data. Finally, we hierarchically integrate USTA and CSTA to grasp the deeper facial cues. Our model emphasizes temporal modeling without neglecting the processing of special data, and it fuses the contents in different modalities while maintaining their respective uniqueness. Extensive experiments on the four benchmarks show the effectiveness of our proposed HSTA. Specifically, compared with the latest method on the CASME3 dataset, it achieves about 3% score improvement in seven-category classification.