Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis

The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conve...

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Veröffentlicht in:Applied sciences Jg. 14; H. 5; S. 1920
Hauptverfasser: Shakhovska, Nataliya, Zherebetskyi, Oleh, Lupenko, Serhii
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2024
Schlagworte:
Accuracy
Algorithms
Artificial intelligence
convolution neural network
Datasets
emotional state
Emotions
Information systems
late fusion
multi-modal emotion recognition
multimodal data
Neural networks
ISSN:2076-3417, 2076-3417
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Zusammenfassung:The paper aims to develop an information system for human emotion recognition in streaming data obtained from a PC or smartphone camera, using different methods of modality merging (image, sound and text). The objects of research are the facial expressions, the emotional color of the tone of a conversation and the text transmitted by a person. The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data. The analysis determined that the best classification accuracy is obtained for systems with data fusion after processing each channel separately and obtaining individual characteristics. The final analysis of the model based on data from a camera and microphone or recording or broadcast of the screen, which were received in the “live” mode, gave a clear understanding that the quality of the obtained results is highly dependent on the quality of the data preparation and labeling. This is directly related to the fact that the data on which the neural network is trained is highly qualified. The neural network with combined data on the penultimate layer allows a psycho-emotional state recognition accuracy of 0.90 to be obtained. The spatial distribution of emotion analysis was also analyzed for each data modality. The model with late fusion of multimodal data demonstrated the best recognition accuracy.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app14051920