GLaRE: A Graph-based Landmark Region Embedding Network for Emotion Recognition
This addresses emotion recognition for applications like human-computer interaction, but it is incremental as it builds on existing graph neural network methods.
The paper tackled facial expression recognition by proposing GLaRE, a graph-based network that uses hierarchical coarsening of facial landmarks, achieving 64.89% accuracy on AffectNet and 94.24% on FERG.
Facial expression recognition (FER) is a crucial task in computer vision with wide range of applications including human computer interaction, surveillance, and assistive technologies. However, challenges such as occlusion, expression variability, and lack of interpretability hinder the performance of traditional FER systems. Graph Neural Networks (GNNs) offer a powerful alternative by modeling relational dependencies between facial landmarks, enabling structured and interpretable learning. In this paper, we propose GLaRE, a novel Graph-based Landmark Region Embedding network for emotion recognition. Facial landmarks are extracted using 3D facial alignment, and a quotient graph is constructed via hierarchical coarsening to preserve spatial structure while reducing complexity. Our method achieves 64.89 percentage accuracy on AffectNet and 94.24 percentage on FERG, outperforming several existing baselines. Additionally, ablation studies have demonstrated that region-level embeddings from quotient graphs have contributed to improved prediction performance.