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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| Vydané v: | Applied sciences Ročník 14; číslo 5; s. 1920 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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MDPI AG
01.03.2024
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| ISSN: | 2076-3417, 2076-3417 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Shakhovska, Nataliya Zherebetskyi, Oleh Lupenko, Serhii |
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| Cites_doi | 10.1016/j.egypro.2019.01.951 10.1016/j.neucom.2017.10.009 10.1016/j.ijepes.2021.107788 10.1016/j.ijcip.2021.100484 10.18653/v1/W18-3302 10.1016/j.cose.2021.102316 10.1016/j.epsr.2021.107732 10.3390/info12090342 10.1007/s00521-020-04748-3 10.18653/v1/D18-1014 10.1016/j.ijepes.2021.107790 10.1016/j.knosys.2018.07.041 10.1109/TAFFC.2020.2988455 10.1016/j.isatra.2021.11.033 10.1145/3410530.3414395 10.1049/iet-cvi.2015.0273 10.3390/app14020558 10.3389/fphys.2021.643202 10.18653/v1/2020.challengehml-1.1 10.1145/3432207 10.1016/j.imr.2016.03.004 10.1145/3219819.3219853 |
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| SubjectTerms | Accuracy Algorithms Artificial intelligence convolution neural network Datasets emotional state Emotions Information systems late fusion multi-modal emotion recognition multimodal data Neural networks |
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| Title | Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis |
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