A survey on Graph Deep Representation Learning for Facial Expression Recognition

Théo Gueuret, Akrem Sellami, Chaabane Djeraba
11/13/2024
cs.CV

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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.

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Cite this paper

@article{gueuret2024survey,
  title  = {A survey on Graph Deep Representation Learning for Facial Expression Recognition},
  author = {Théo Gueuret and Akrem Sellami and Chaabane Djeraba},
  year   = {2024},
  eprint = {2411.08472},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.08472v1}
}

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