EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition

• URL: http://arxiv.org/abs/2501.08199v1
• Date: Tue, 14 Jan 2025 15:23:36 GMT
• Title: EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
• Authors: Yassine El Boudouri, Amine Bohi,
• Abstract summary: EmoNeXt is a novel deep learning framework for facial expression recognition based on an adapted ConvNeXt architecture network. We demonstrate the superiority of our model over existing state-of-the-art deep learning models on the FER2013 dataset regarding emotion classification accuracy.
• Score: 0.0
• License:
• 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.

Related papers

• Emotion Detection through Body Gesture and Face [0.0]
  The project addresses the challenge of emotion recognition by focusing on non-facial cues, specifically hands, body gestures, and gestures. Traditional emotion recognition systems mainly rely on facial expression analysis and often ignore the rich emotional information conveyed through body language. The project aims to contribute to the field of affective computing by enhancing the ability of machines to interpret and respond to human emotions in a more comprehensive and nuanced way.
  arXiv  Detail & Related papers  (2024-07-13T15:15:50Z)
• Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News [21.707761612280304]
  We present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resource-intensive, whereas our approach with Facial Landmark Emotion Recognition (FLER) offers a simplified yet effective alternative.
  arXiv  Detail & Related papers  (2024-04-21T00:14:03Z)
• Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models [49.3179290313959]
  The proposed method, emotion-centered generative replay (ECgr), tackles this challenge by integrating synthetic images from generative adversarial networks. ECgr incorporates a quality assurance algorithm to ensure the fidelity of generated images. The experimental results on four diverse facial expression datasets demonstrate that incorporating images generated by our pseudo-rehearsal method enhances training on the targeted dataset and the source dataset.
  arXiv  Detail & Related papers  (2024-04-18T15:28:34Z)
• Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces [2.4158349218144393]
  We propose the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system.
  arXiv  Detail & Related papers  (2023-11-20T18:14:53Z)
• Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers [57.1091606948826]
  We propose a novel FER model, named Poker Face Vision Transformer or PF-ViT, to address these challenges. PF-ViT aims to separate and recognize the disturbance-agnostic emotion from a static facial image via generating its corresponding poker face. PF-ViT utilizes vanilla Vision Transformers, and its components are pre-trained as Masked Autoencoders on a large facial expression dataset.
  arXiv  Detail & Related papers  (2022-07-22T13:39:06Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Leveraging Recent Advances in Deep Learning for Audio-Visual Emotion Recognition [2.1485350418225244]
  Spontaneous multi-modal emotion recognition has been extensively studied for human behavior analysis. We propose a new deep learning-based approach for audio-visual emotion recognition.
  arXiv  Detail & Related papers  (2021-03-16T15:49:15Z)
• Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks [82.54695985117783]
  We investigate the suitability of state-of-the-art deep learning architectures for continuous emotion recognition using long video sequences captured in-the-wild. We have developed and evaluated convolutional recurrent neural networks combining 2D-CNNs and long short term-memory units, and inflated 3D-CNN models, which are built by inflating the weights of a pre-trained 2D-CNN model during fine-tuning.
  arXiv  Detail & Related papers  (2020-11-18T13:42:05Z)
• Facial Expression Editing with Continuous Emotion Labels [76.36392210528105]
  Deep generative models have achieved impressive results in the field of automated facial expression editing. We propose a model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels.
  arXiv  Detail & Related papers  (2020-06-22T13:03:02Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)
• An adversarial learning framework for preserving users' anonymity in face-based emotion recognition [6.9581841997309475]
  This paper proposes an adversarial learning framework which relies on a convolutional neural network (CNN) architecture trained through an iterative procedure. Results indicate that the proposed approach can learn a convolutional transformation for preserving emotion recognition accuracy and degrading face identity recognition.
  arXiv  Detail & Related papers  (2020-01-16T22:45:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.