CG-MER: A Card Game-based Multimodal dataset for Emotion Recognition

• URL: http://arxiv.org/abs/2501.08182v1
• Date: Tue, 14 Jan 2025 15:08:56 GMT
• Title: CG-MER: A Card Game-based Multimodal dataset for Emotion Recognition
• Authors: Nessrine Farhat, Amine Bohi, Leila Ben Letaifa, Rim Slama,
• Abstract summary: This paper introduces a comprehensive French multimodal dataset designed specifically for emotion recognition. The dataset encompasses three primary modalities: facial expressions, speech, and gestures, providing a holistic perspective on emotions. The dataset has the potential to incorporate additional modalities, such as Natural Language Processing (NLP) to expand the scope of emotion recognition research.
• Score: 0.0
• License:
• Abstract: The field of affective computing has seen significant advancements in exploring the relationship between emotions and emerging technologies. This paper presents a novel and valuable contribution to this field with the introduction of a comprehensive French multimodal dataset designed specifically for emotion recognition. The dataset encompasses three primary modalities: facial expressions, speech, and gestures, providing a holistic perspective on emotions. Moreover, the dataset has the potential to incorporate additional modalities, such as Natural Language Processing (NLP) to expand the scope of emotion recognition research. The dataset was curated through engaging participants in card game sessions, where they were prompted to express a range of emotions while responding to diverse questions. The study included 10 sessions with 20 participants (9 females and 11 males). The dataset serves as a valuable resource for furthering research in emotion recognition and provides an avenue for exploring the intricate connections between human emotions and digital technologies.

Related papers

• SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations [53.60993109543582]
  SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE) In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
  arXiv  Detail & Related papers  (2024-05-19T09:59:00Z)
• Exploring Emotions in Multi-componential Space using Interactive VR Games [1.1510009152620668]
  We operationalised a data-driven approach using interactive Virtual Reality (VR) games. We used Machine Learning (ML) methods to identify the unique contributions of each component to emotion differentiation. These findings also have implications for using VR environments in emotion research.
  arXiv  Detail & Related papers  (2024-04-04T06:54:44Z)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)
• Face Emotion Recognization Using Dataset Augmentation Based on Neural Network [0.0]
  Facial expression is one of the most external indications of a person's feelings and emotions. It plays an important role in coordinating interpersonal relationships. As a branch of the field of analyzing sentiment, facial expression recognition offers broad application prospects.
  arXiv  Detail & Related papers  (2022-10-23T10:21:45Z)
• 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)
• Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies [106.62835060095532]
  We discuss several key aspects of multi-modal emotion recognition (MER) We begin with a brief introduction on widely used emotion representation models and affective modalities. We then summarize existing emotion annotation strategies and corresponding computational tasks. Finally, we outline several real-world applications and discuss some future directions.
  arXiv  Detail & Related papers  (2021-08-18T21:55:20Z)
• Affective Image Content Analysis: Two Decades Review and New Perspectives [132.889649256384]
  We will comprehensively review the development of affective image content analysis (AICA) in the recent two decades. We will focus on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
  arXiv  Detail & Related papers  (2021-06-30T15:20:56Z)
• Temporal aggregation of audio-visual modalities for emotion recognition [0.5352699766206808]
  We propose a multimodal fusion technique for emotion recognition based on combining audio-visual modalities from a temporal window with different temporal offsets for each modality. Our proposed method outperforms other methods from the literature and human accuracy rating.
  arXiv  Detail & Related papers  (2020-07-08T18:44:15Z)
• K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations [19.350031493515562]
  K-EmoCon is a novel dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals. It includes emotion annotations from all three available perspectives: self, debate partner, and external observers.
  arXiv  Detail & Related papers  (2020-05-08T15:51:12Z)
• Context Based Emotion Recognition using EMOTIC Dataset [22.631542327834595]
  We present EMOTIC, a dataset of images of people annotated with their apparent emotion. Using the EMOTIC dataset we train different CNN models for emotion recognition. Our results show how scene context provides important information to automatically recognize emotional states.
  arXiv  Detail & Related papers  (2020-03-30T12:38:50Z)
• 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.