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

TL;DR

This paper introduces CG-MER, a comprehensive French multimodal dataset collected through card game sessions, capturing facial expressions, speech, and gestures for advancing emotion recognition research.

Contribution

It presents a novel multimodal dataset specifically designed for emotion recognition, incorporating multiple modalities and collected via engaging card game sessions.

Findings

Dataset includes facial, speech, and gesture data from 20 participants.

Potential for expanding modalities with NLP techniques.

Provides a new resource for emotion recognition research.

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.