EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
This work addresses facial emotion recognition for human-computer interaction applications, representing an incremental improvement over existing methods.
The authors tackled facial emotion recognition by proposing EmoNeXt, an adapted ConvNeXt architecture with Spatial Transformer Networks and Squeeze-and-Excitation blocks, achieving superior emotion classification accuracy on the FER2013 dataset compared to existing state-of-the-art models.
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.