A survey on Graph Deep Representation Learning for Facial Expression Recognition

Théo Gueuret (1) , Akrem Sellami (1) , Chaabane Djeraba (2, 1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 IRCICA - Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancée - UAR 3380

This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL). Initially, we introduce the task of FER and the concepts of graph representation and GRL. Afterward, we discuss some of the most prevalent and valuable databases for this task. We explore promising approaches for graph representation in FER, including graph diffusion, spatio-temporal graphs, and multi-stream architectures. Finally, we identify future research opportunities and provide concluding remarks.

Mots clés: Facial Expression Recognition; Graph Representation Learning

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Théo Gueuret, Akrem Sellami, Chaabane Djeraba. A survey on Graph Deep Representation Learning for Facial Expression Recognition. International Conference on Content-based Multimedia Indexing, Sep 2024, Reykjavík, Iceland. ⟨hal-04768316⟩