From Continuous Affective Space to Continuous Expression Space: Non-Verbal Behaviour Recognition and Generation

TitleFrom Continuous Affective Space to Continuous Expression Space: Non-Verbal Behaviour Recognition and Generation
Publication TypeConference Paper
Year of Publication2014
AuthorsZhong, J., & Cañamero L.
Name of ProceedingsProc. 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob 2014)
Pagination75–80
Date Published10/2014
PublisherIEEE
Conference LocationGenoa, Italy
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.

URLhttp://ieeexplore.ieee.org/document/6982957/
DOI10.1109/DEVLRN.2014.6982957