Papers › EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

14 Jan 2025IEEE 25th International Workshop on Multimedia Signal Processing (MMSP) 2023 9arXiv:2501.08199archive 2025-07-28

Yassine El Boudouri, Amine Bohi

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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Tasks

Deep LearningEmotion ClassificationEmotion RecognitionFacial Emotion RecognitionFacial Expression Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Facial Expression Recognition (FER) FER2013 EmoNeXt Accuracy 76.12 #6 of 17 Archive leaderboard report

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Methods

Absolute Position EncodingsAdamAttentionBPEConvNeXtDense ConnectionsDropoutFocusLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxSpatial TransformerTransformer

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