@inproceedings {2014, title = {From Continuous Affective Space to Continuous Expression Space: Non-Verbal Behaviour Recognition and Generation}, booktitle = {Proc. 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob 2014)}, year = {2014}, note = {Download}, month = {10/2014}, pages = {75{\textendash}80}, publisher = {IEEE}, organization = {IEEE}, address = {Genoa, 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.}, doi = {10.1109/DEVLRN.2014.6982957}, url = {http://ieeexplore.ieee.org/document/6982957/}, author = {Junpei Zhong and Lola Ca{\~n}amero} }