EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition

Andrey V. Savchenko
3/16/2023
cs.CV

No official code found. Best implementation: sb-ai-lab/EmotiEffLib (1,057 stars, Apache-2.0, last commit 2026-03-16).

Abstract

In this article, the results of our team for the fifth Affective Behavior Analysis in-the-wild (ABAW) competition are presented. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model achieves a much greater macro-averaged F1-score for facial expression recognition and action unit detection and concordance correlation coefficients for valence/arousal estimation when compared to baseline.

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Code Implementations(1)

sb-ai-lab/EmotiEffLibCommunity100%
Apache-2.0

Efficient face emotion recognition in photos and videos

1,057156C++, JavaMar 17, 20217 months agoApache-2.0
emotion-analysisemotion-detectionemotion-recognitionface-emotion-detectionface-emotion-recognition+3 more

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Cite this paper

@article{savchenko2023emotieffnet,
  title  = {EmotiEffNet Facial Features in Uni-task Emotion Recognition in Video at ABAW-5 competition},
  author = {Andrey V. Savchenko},
  year   = {2023},
  eprint = {2303.09162},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.09162v1}
}

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