mlsanityData to 7 Oct 2026

EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

arXiv 2501.08199 ↗First posted 14 Jan 2025cs.CV cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

23 of 41 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysiscited by 1
  2. 2025Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Designcited by 0
  3. 2025Achieving 3D Attention via Triplet Squeeze and Excitation Blockcited by 0
  4. 2025AGCD-Net: Attention Guided Context Debiasing Network for Emotion Recognitioncited by 0