# AU-aware graph convolutional network for Macro- and Micro-expression spotting

Source: [AU-aware graph convolutional network for Macro- and Micro-expression spotting](<https://www.sota2.com/research/paper/68fa6ec51e209131cea41f26>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:21:27.234Z

Authors: Shukang Yin, Shiwei Wu, Tong Xu, Shifeng Liu, Sirui Zhao, Enhong Chen

Published: 2023-03-16T07:00:36.000Z

## Abstract

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and relevant facial muscles. To address this issue for better performance of ME spotting, this paper seeks to extract finer spatial features by modeling the relationships between facial Regions of Interest (ROIs). Specifically, we propose a graph convolutional-based network, called Action-Unit-aWare Graph Convolutional Network (AUW-GCN). Furthermore, to inject prior information and to cope with the problem of small datasets, AU-related statistics are encoded into the network. Comprehensive experiments show that our results outperform baseline methods consistently and achieve new SOTA performance in two benchmark datasets,CAS(ME)^2 and SAMM-LV. Our code is available at https://github.com/xjtupanda/AUW-GCN.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Spotting on SAMMLV](<https://www.sota2.com/research/sota/spotting-on-sammlv.md>): Spotting; SAMMLV; F1 Score: 42.9.

- [Facial Expression Spotting on CAS(ME)2](<https://www.sota2.com/research/sota/facial-expression-spotting-on-cas-me-2.md>): Facial Expression Spotting; CAS(ME)2; Overall F1-score: 38.34.

- [Facial expression spotting on SAMM-LV](<https://www.sota2.com/research/sota/facial-expression-spotting-on-samm-lv.md>): Facial Expression Spotting; SAMM-LV; ME F1-score: 19.84.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.09114>)

[PDF](<https://arxiv.org/pdf/2303.09114.pdf>)