Details

Autor(en) / Beteiligte

• Preema, P. Y.
• Chandra, J.
• Joseph, Alwin
• Ray, Sudeshna
• Karras, Dimitrios A
• Oruganti, Sai Kiran

Titel

A Review on Multi-Modal Classification for Emotional Intelligence

Ist Teil von

• Engineering, Science, and Sustainability, 2024, p.118-122

Auflage

1

Ort / Verlag

United Kingdom: CRC Press

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Emotions are unavoidable in human beings and play an essential role in perceiving things. This paper reviews various methods used to capture human emotions effectively. The work analyses the effectiveness of unimodal, bimodal, and multimodal machine learning models created for the processing of emotions. These models effectively handle various signals and input modalities, including physiological and audio-visual signals, from which emotions are captured and analyzed. The study concludes that machine learning algorithms, including Support Vector Machine, K-Nearest Neighbour, Gaussian Mixture Model, and Convolutional Neural Network, are considered popular methods for processing the various inputs for classifying emotions from multiple data sources. The research also identifies that the combination of EEG and eye movements could give better accuracy compared with other data forms.