# GLaRE: A Graph-based Landmark Region Embedding Network for Emotion Recognition

## TL;DR

GLaRE introduces a graph neural network approach that models facial landmarks hierarchically for improved emotion recognition, achieving state-of-the-art accuracy and interpretability in FER tasks.

## Contribution

The paper presents a novel graph-based network that leverages hierarchical facial landmark embeddings for enhanced emotion recognition performance.

## Key findings

- Achieves 64.89% accuracy on AffectNet
- Achieves 94.24% accuracy on FERG
- Region-level embeddings improve prediction accuracy

## Abstract

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.