Emotion Detection through Body Gesture and Face

• URL: http://arxiv.org/abs/2407.09913v1
• Date: Sat, 13 Jul 2024 15:15:50 GMT
• Title: Emotion Detection through Body Gesture and Face
• Authors: Haoyang Liu,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The project leverages advanced machine and deep learning techniques to address 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. To bridge this gap, this method leverages the Aff-Wild2 and DFEW databases to train and evaluate a model capable of recognizing seven basic emotions (angry, disgust, fear, happiness, sadness, surprise, and neutral) and estimating valence and continuous scales wakeup descriptor. Leverage OpenPose for pose estimation to extract detailed body posture and posture features from images and videos. These features serve as input to state-of-the-art neural network architectures, including ResNet, and ANN for emotion classification, and fully connected layers for valence arousal regression analysis. This bifurcation strategy can solve classification and regression problems in the field of emotion recognition. 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. By integrating multimodal data and cutting-edge computational models, I aspire to develop a system that not only enriches human-computer interaction but also has potential applications in areas as diverse as mental health support, educational technology, and autonomous vehicle systems.

Related papers

• 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)
• Facial Expression Recognition using Squeeze and Excitation-powered Swin Transformers [0.0]
  We propose a framework that employs Swin Vision Transformers (SwinT) and squeeze and excitation block (SE) to address vision tasks. Our focus was to create an efficient FER model based on SwinT architecture that can recognize facial emotions using minimal data. We trained our model on a hybrid dataset and evaluated its performance on the AffectNet dataset, achieving an F1-score of 0.5420.
  arXiv  Detail & Related papers  (2023-01-26T02:29:17Z)
• 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)
• Multi-Cue Adaptive Emotion Recognition Network [4.570705738465714]
  We propose a new deep learning approach for emotion recognition based on adaptive multi-cues. We compare the proposed approach with the state-of-art approaches in the CAER-S dataset.
  arXiv  Detail & Related papers  (2021-11-03T15:08:55Z)
• SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
  We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
  arXiv  Detail & Related papers  (2021-10-24T02:41:41Z)
• Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
  We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
  arXiv  Detail & Related papers  (2021-09-04T08:14:52Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• 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)
• 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.