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Communication Dans Un Congrès Année : 2023

EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

Résumé

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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Dates et versions

hal-04543344 , version 1 (12-04-2024)

Identifiants

Citer

Yassine El Boudouri, Amine Bohi. EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition. 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Sep 2023, Poitiers, France. pp.1-6, ⟨10.1109/MMSP59012.2023.10337732⟩. ⟨hal-04543344⟩
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