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Bimodal emotion recognition based on facial expression and speech
KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID. College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China.ORCID iD: 0000-0003-3779-5647
2018 (English)In: Journal of Nanjing University of Posts and Telecommunications, ISSN 1673-5439, Vol. 38, no 1, p. 60-65Article in journal (Refereed) Published
Abstract [en]

In the area of future artificial intelligence, the emotion recognition of the computers will play a more important role. For the bimodal emotion recognition from facial expression and speech, a feature fusion method based on sparse canonical correlation analysis is presented. Firstly, the emotion features from facial expression and speech are respectively extract. Then, the parse canonical correlation analysis is used to fuse the bimodal emotion features. Finally, the K-nearest neighbor classifier is used for emotion recognition. The experimental results show that the bimodal method based on the sparse canonical correlation analysis can obtain better recognition rate than the speech and the facial expression with single modal.

Place, publisher, year, edition, pages
Journal of Nanjing Institute of Posts and Telecommunications , 2018. Vol. 38, no 1, p. 60-65
Keywords [en]
Bimodal emotion recognition, Facial expression, Sparse canonical correlation analysis, Speech
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247225DOI: 10.14132/j.cnki.1673-5439.2018.01.007Scopus ID: 2-s2.0-85056809780OAI: oai:DiVA.org:kth-247225DiVA, id: diva2:1301924
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved

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Li, Haibo

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  • apa
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  • de-DE
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  • en-US
  • fi-FI
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf