mlsanityData to 7 Oct 2026

Facial Expression Recognition with Swin Transformer

arXiv 2203.13472 ↗First posted 25 Mar 2022cs.CV cs.AIcited by 9

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Abstract (arXiv)

The task of recognizing human facial expressions plays a vital role in various human-related systems, including health care and medical fields. With the recent success of deep learning and the accessibility of a large amount of annotated data, facial expression recognition research has been mature enough to be utilized in real-world scenarios with audio-visual datasets. In this paper, we introduce Swin transformer-based facial expression approach for an in-the-wild audio-visual dataset of the Aff-Wild2 Expression dataset. Specifically, we employ a three-stream network (i.e., Visual stream, Temporal stream, and Audio stream) for the audio-visual videos to fuse the multi-modal information into facial expression recognition. Experimental results on the Aff-Wild2 dataset show the effectiveness of our proposed multi-modal approaches.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 25 Mar 2022.

Builds on

11 of 17 references.

References · 11

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  2. 2018AutoAugment: Learning Augmentation Policies from Datacited by 728
  3. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  4. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  5. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  6. 2021Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Frameworkcited by 174
  7. 2021Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Studycited by 168
  8. 2021Analysing Affective Behavior in the second ABAW2 Competitioncited by 163
  9. 2022ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challengescited by 156
  10. 2019Face Behavior a la carte: Expressions, Affect and Action Units in a Single Networkcited by 155
  11. 2018Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applicationscited by 21

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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 3.

9 papers.

  1. 2022ABAW: Learning from Synthetic Data & Multi-Task Learning Challengescited by 110
  2. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  3. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  4. 2023Multi-modal Facial Affective Analysis based on Masked Autoencodercited by 23
  5. 2024Robust Light-Weight Facial Affective Behavior Recognition with CLIPcited by 16
  6. 2023MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videoscited by 11
  7. 2022Affective Behaviour Analysis Using Pretrained Model with Facial Prioricited by 10
  8. 2023Facial Affect Recognition based on Transformer Encoder and Audiovisual Fusion for the ABAW5 Challengecited by 6
  9. 2024Multimodal Fusion Method with Spatiotemporal Sequences and Relationship Learning for Valence-Arousal Estimationcited by 5