From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation

Junpei Zhong, Lola Canamero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this research, a recurrent neural network with parametric bias (RNNPB) was adopted to construct a continuous expression space from emotion caused human behaviours. It made use of the short-term memory ability of the recurrent weights to store spatio-temporal sequences features, while the attached parametric bias units were trained in a self-organizing way and represented as a low-dimensional expression space to capture these non-linear features of the sequences. Three demonstrations were given: training and recognition performances were examined in computer simulations, while the network generated both trained and novel movements were shown in a three-dimensional avatar demonstrations.

Original languageEnglish
Title of host publicationIEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-80
Number of pages6
ISBN (Electronic)9781479975402
DOIs
Publication statusPublished - 2014 Dec 11
Externally publishedYes
Event4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014 - Genoa
Duration: 2014 Oct 132014 Oct 16

Other

Other4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014
CityGenoa
Period14/10/1314/10/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Zhong, J., & Canamero, L. (2014). From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation. In IEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (pp. 75-80). [6982957] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DEVLRN.2014.6982957