Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

TL;DR

This paper introduces a spatio-temporal graph-based method for facial action units detection, leveraging a pre-trained facial encoder and AU-specific features to improve recognition accuracy in wild conditions.

Contribution

It proposes a novel spatio-temporal graph learning framework that models spatial and temporal AU relationships for enhanced detection performance.

Findings

Outperformed baseline methods in AU detection accuracy.

Achieved 4th place in the ABAW competition AU recognition track.

Demonstrated the effectiveness of spatio-temporal graph modeling in wild scenarios.

Abstract

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://github.com/wzh125/ABAW-5.