Entropy-Assisted Emotion Recognition of Valence and Arousal Using XGBoost Classifier - Artificial Intelligence Applications and Innovations (AIAI 2018) Access content directly
Conference Papers Year : 2018

Entropy-Assisted Emotion Recognition of Valence and Arousal Using XGBoost Classifier

Sheng-Hui Wang
  • Function : Author
  • PersonId : 1033551
Huai-Ting Li
  • Function : Author
  • PersonId : 1033552
En-Jui Chang
  • Function : Author
  • PersonId : 1033553
An-Yeu (andy) Wu
  • Function : Author
  • PersonId : 1033554

Abstract

Emotion recognition is an essential function to realize human-machine interaction devices. Physiological signals which can be collected easily and continuously by wearable sensors are good inputs for emotion analysis. How to effectively process physiological signals, extract critical features, and choose machine learning model for emotion classification has been a big challenge. In this paper, an entropy-based processing scheme for emotion recognition framework is proposed, which includes entropy domain feature extraction and prediction by XGBoost classifier. We experiment on AMIGOS database and the experimental results show that the proposed scheme for multi-modal analysis outperforms conventional processing approaches. It achieves approximately 80% and 68% accuracy of prediction for two affect dimensions, valence and arousal. For one modality case, we found that galvanic skin response (GSR) channel is the most potential modality for prediction, which leads to best performances.
Fichier principal
Vignette du fichier
467708_1_En_22_Chapter.pdf (545.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01821082 , version 1 (22-06-2018)

Licence

Attribution

Identifiers

Cite

Sheng-Hui Wang, Huai-Ting Li, En-Jui Chang, An-Yeu (andy) Wu. Entropy-Assisted Emotion Recognition of Valence and Arousal Using XGBoost Classifier. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.249-260, ⟨10.1007/978-3-319-92007-8_22⟩. ⟨hal-01821082⟩
149 View
166 Download

Altmetric

Share

Gmail Facebook X LinkedIn More