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A Survey on Graph Deep Representation Learning for Facial Expression Recognition

Year: 2024

Type: conference-paper

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.

Source: International Conference on Content-Based Multimedia Indexing (CBMI)

DOI: 10.1109/cbmi62980.2024.10858877

Authors: Théo Gueuret, Akrem Sellami, Chaabane Djeraba

Institutions: Centre National de la Recherche Scientifique, Université de Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille, Centrale Lille Institut

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: İslam Tarih, Sanat ve Kültür Araştırma Merkezi

OpenAlex ID: W4407129207

FWCI: 1.256

Cites: 46

Cited by: 2

Related to: 10