EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

Yassine El Boudouri, Amine Bohi
1/14/2025
cs.CVcs.AI

No official code found. Best implementation: yelboudouri/EmoNeXt (48 stars, PyTorch, last commit 2024-01-18).

Abstract

Facial expressions play a crucial role in human communication serving as a powerful and impactful means to express a wide range of emotions. With advancements in artificial intelligence and computer vision, deep neural networks have emerged as effective tools for facial emotion recognition. In this paper, we propose EmoNeXt, a novel deep learning framework for facial expression recognition based on an adapted ConvNeXt architecture network. We integrate a Spatial Transformer Network (STN) to focus on feature-rich regions of the face and Squeeze-and-Excitation blocks to capture channel-wise dependencies. Moreover, we introduce a self-attention regularization term, encouraging the model to generate compact feature vectors. We demonstrate the superiority of our model over existing state-of-the-art deep learning models on the FER2013 dataset regarding emotion classification accuracy.

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Code Implementations(1)

yelboudouri/EmoNeXtCommunity100%
PyTorch

Code for the paper: "EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition"

4818PythonJun 12, 20232 years ago
emotion-recognitionpaperpytorch

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Cite this paper

@article{boudouri2025emonext,
  title  = {EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition},
  author = {Yassine El Boudouri and Amine Bohi},
  year   = {2025},
  eprint = {2501.08199},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.08199v1}
}

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