A survey on Graph Deep Representation Learning for Facial Expression Recognition

TL;DR

This comprehensive review delves deeply into the various methodologies applied to facial expression recognition (FER) through the lens of graph representation learning (GRL) and explores promising approaches for graph representation in FER.

Abstract

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.

Figures (3)

• Figure 1: Samples from the JAFFE jaffe_1998 FER database.
• Figure 2: Königsberg bridge problem pasqualini2024.
• Figure 3: Difference between CNN and GNN wu2020