mlsanityData to 7 Oct 2026

Expression Recognition Analysis in the Wild

arXiv 2101.09231 ↗First posted 22 Jan 2021cs.CV cs.HC cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Facial Expression Recognition(FER) is one of the most important topic in Human-Computer interactions(HCI). In this work we report details and experimental results about a facial expression recognition method based on state-of-the-art methods. We fine-tuned a SeNet deep learning architecture pre-trained on the well-known VGGFace2 dataset, on the AffWild2 facial expression recognition dataset. The main goal of this work is to define a baseline for a novel method we are going to propose in the near future. This paper is also required by the Affective Behavior Analysis in-the-wild (ABAW) competition in order to evaluate on the test set this approach. The results reported here are on the validation set and are related on the Expression Challenge part (seven basic emotion recognition) of the competition. We will update them as soon as the actual results on the test set will be published on the leaderboard.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 22 Jan 2021.

Builds on

11 of 19 references.

References · 11

  1. 2017Squeeze-and-Excitation Networkscited by 4,763
  2. 2016An overview of gradient descent optimization algorithmscited by 1,021
  3. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  4. 2019Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFacecited by 222
  5. 2018Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyondcited by 216
  6. 2020Analysing Affective Behavior in the First ABAW 2020 Competitioncited by 178
  7. 2018Aff-Wild2: Extending the Aff-Wild Database for Affect Recognitioncited by 80
  8. 2018A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressionscited by 25
  9. 2019Deep Learning for Human Affect Recognition: Insights and New Developmentscited by 23
  10. 2019Cross-Resolution Learning for Face Recognitioncited by 22
  11. 2017A Deep Learning Perspective on the Origin of Facial Expressionscited by 6

Built on by

Not cited yet.