Emotion Classification of Children Expressions

• URL: http://arxiv.org/abs/2411.07708v1
• Date: Tue, 12 Nov 2024 10:47:31 GMT
• Title: Emotion Classification of Children Expressions
• Authors: Sanchayan Vivekananthan,
• Abstract summary: The model developed is achieved by using advanced concepts of models with Squeeze-andExcitation blocks, Convolutional Block Attention modules, and robust data augmentation. The model designed using Batch Normalisation, Dropout, and SE Attention mechanisms for the classification of children's emotions achieved an accuracy rate of 89%.
• Score: 0.0
• License:
• Abstract: This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion recognition systems algorithms primarily train on adult faces, the model developed is achieved by using advanced concepts of models with Squeeze-andExcitation blocks, Convolutional Block Attention modules, and robust data augmentation. Stable Diffusion image synthesis was used for expanding and diversifying the data set generating realistic and various training samples. The model designed using Batch Normalisation, Dropout, and SE Attention mechanisms for the classification of children's emotions achieved an accuracy rate of 89\% due to these methods improving the precision of emotion recognition in children. The relative importance of this issue is raised in this study with an emphasis on the call for a more specific model in emotion detection systems for the young generation with specific direction on how the young people can be assisted to manage emotions while online.

Related papers

• Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques [0.0]
  This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset. We employed a range of models for this task, including six machine learning models, three ensemble models, and a Long Short-Term Memory (LSTM) model. Results indicate that the Stacking classifier outperforms other models in accuracy and performance.
  arXiv  Detail & Related papers  (2024-11-15T16:28:25Z)
• Leaving Some Facial Features Behind [0.0]
  This study examines how specific facial features influence emotion classification, using facial perturbations on the Fer2013 dataset. Models trained on data with the removal of some important facial feature experienced up to an 85% accuracy drop when compared to baseline for emotions like happy and surprise.
  arXiv  Detail & Related papers  (2024-10-29T02:28:53Z)
• Generative Technology for Human Emotion Recognition: A Scope Review [11.578408396744237]
  This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 320 research papers until June 2024. It will introduce the mathematical principles of different generative models and the commonly used datasets. It will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities.
  arXiv  Detail & Related papers  (2024-07-04T05:22:55Z)
• Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation [5.534160116442057]
  The subjectivity of emotions poses significant challenges in developing accurate and robust computational models. This thesis examines critical facets of emotion recognition, beginning with the collection of diverse datasets. To handle the challenge of non-representative training data, this work collects the Multimodal Stressed Emotion dataset.
  arXiv  Detail & Related papers  (2023-09-06T02:45:42Z)
• 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)
• 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)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)
• Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study [5.258326585054865]
  Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. Most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models.
  arXiv  Detail & Related papers  (2020-12-16T00:08:51Z)
• Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition [55.44502358463217]
  We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues. Our model achieves state-of-the-art performance on most of the emotion categories. Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
  arXiv  Detail & Related papers  (2020-09-21T06:10:39Z)
• 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)

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.