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A survey on Graph Deep Representation Learning for Facial Expression Recognition

arXiv 2411.08472 ↗First posted 13 Nov 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 50 references that are in the map. First posted on arXiv 13 Nov 2024.

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13 of 50 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  5. 2020DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wildcited by 128
  6. 2022FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videoscited by 69
  7. 2022MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wildcited by 65
  8. 2020Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learningcited by 46
  9. 2018Learning-based Video Motion Magnificationcited by 43
  10. 2015Learning Social Relation Traits from Face Imagescited by 25
  11. 2024A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIPcited by 14
  12. 2024MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wildcited by 14
1 more
  1. 2021Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognitioncited by 12

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