  Social robots able to continually learn facial expressions could
progressively improve their emotion recognition capability towards people
interacting with them. Semi-supervised learning through ensemble predictions is
an efficient strategy to leverage the high exposure of unlabelled facial
expressions during human-robot interactions. Traditional ensemble-based
systems, however, are composed of several independent classifiers leading to a
high degree of redundancy, and unnecessary allocation of computational
resources. In this paper, we proposed an ensemble based on convolutional
networks where the early layers are strong low-level feature extractors, and
their representations shared with an ensemble of convolutional branches. This
results in a significant drop in redundancy of low-level features processing.
Training in a semi-supervised setting, we show that our approach is able to
continually learn facial expressions through ensemble predictions using
unlabelled samples from different data distributions.
