{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/emonext-an-adapted-convnext-for-facial-1","title":"EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition","arxiv_id":"2501.08199","date":"2025-01-14","proceeding":"IEEE 25th International Workshop on Multimedia Signal Processing (MMSP) 2023 9","authors":["Yassine El Boudouri","Amine Bohi"],"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.","url_abs":"https://arxiv.org/abs/2501.08199v1","url_pdf":"https://arxiv.org/pdf/2501.08199v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"emonext-an-adapted-convnext-for-facial-1","repo_url":"https://github.com/yelboudouri/EmoNeXt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"facial-emotion-recognition","task_name":"Facial Emotion Recognition"},{"task_slug":"facial-expression-recognition-1","task_name":"Facial Expression Recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convnext","method_name":"ConvNeXt"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"spatial-transformer","method_name":"Spatial Transformer"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/facial-expression-recognition-on-fer2013","task":"Facial Expression Recognition (FER)","dataset":"FER2013","model":"EmoNeXt","rank_in_archive_order":6,"of":17,"metrics":{"Accuracy":"76.12"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.08199","atlas_url":"https://app.syntology.ai/?focus=2501.08199","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}