Survey on AI-Based Multimodal Methods for Emotion Detection

Abstract : Automatic emotion recognition constitutes one of the great chal-lenges providing new tools for more objective and quicker diagnosis, commu-nication and research. Quick and accurate emotion recognition may increasepossibilities of computers, robots, and integrated environments to recognizehuman emotions, and response accordingly to them a social rules. The purposeof this paper is to investigate the possibility of automated emotion representa-tion, recognition and prediction its state-of-the-art and main directions for fur-ther research. We focus on the impact of emotion analysis and state of the arts ofmultimodal emotion detection. We present existing works, possibilities andexisting methods to analyze emotion in text, sound, image, video and physio-logical signals. We also emphasize the most important features for all availableemotion recognition modes. Finally, we present the available platform andoutlines the existing projects, which deal with multimodal emotion analysis.
Liste complète des métadonnées

Littérature citée [40 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-02135811
Contributeur : Claire Medrala <>
Soumis le : mardi 21 mai 2019 - 15:38:04
Dernière modification le : mercredi 29 mai 2019 - 01:47:50

Fichier

A-715.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : hal-02135811, version 1

Citation

Catherine Maréchal, Dariusz Mikołajewski, Krzysztof Tyburek, Piotr Prokopowicz, Lamine Bougueroua, et al.. Survey on AI-Based Multimodal Methods for Emotion Detection. High-Performance Modelling and Simulation for Big Data Applications, Springer, pp 307-324, 2019, 978-3-030-16272-6. ⟨hal-02135811⟩

Partager

Métriques

Consultations de la notice

31

Téléchargements de fichiers

134