EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
arXiv:2501.08199 · doi:10.1109/MMSP59012.2023.10337732
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.
6 pages, 5 figures and 2 tables. 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), Poitiers, France
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