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About Multimodal Emotion Recognition Using Deep Learning Architectures
This document presents a multimodal emotion recognition system using deep learning architectures. It introduces the emoFBVP database containing recordings of facial expressions, body gestures, vocal expressions and physiological signals for actors displaying various emotions. It then describes four deep belief network models for generating robust multimodal features to classify emotions from this database in an unsupervised manner. Convolutional deep belief network models are also proposed to learn salient features for recognizing subtle emotion expressions. Experimental results show these deep learning models outperform state-of-the-art methods for multimodal emotion recognition.
Detailed Information
| Author: | ['Raja Kumar'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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