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About this paper
Théo Gueuret, Akrem Sellami, Chaabane Djeraba
A structured survey categorizes and analyzes Graph Representation Learning (GRL) methodologies for Facial Expression Recognition (FER), detailing approaches like Graph Diffusion, Spatio-temporal Graphs, and Dual-Stream Graphs. This review demonstrates GRL's ability to model complex relational facial data, often achieving competitive or superior performance over traditional methods for dynamic FER.
Key insights
- Graph Representation Learning effectively models the relational and structural information within facial data, addressing limitations of traditional grid-based methods.
- Specific GRL paradigms, such as Graph Diffusion, Spatio-temporal Graphs, and Dual-Stream Graphs, offer tailored solutions for challenges like domain adaptation, dynamic expression modeling, and multi-modal feature fusion.
- GRL-based methods can yield more robust and generalizable Facial Expression Recognition systems, particularly for dynamic and "in-the-wild" scenarios.
Summary by alphaXiv.
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