3 research outputs found
An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions
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
Face Images as Jigsaw Puzzles: Compositional Perception of Human Faces for Machines Using Generative Adversarial Networks
An important goal in human-robot-interaction (HRI) is for machines to achieve
a close to human level of face perception. One of the important differences
between machine learning and human intelligence is the lack of
compositionality. This paper introduces a new scheme to enable generative
adversarial networks to learn the distribution of face images composed of
smaller parts. This results in a more flexible machine face perception and
easier generalization to outside training examples. We demonstrate that this
model is able to produce realistic high-quality face images by generating and
piecing together the parts. Additionally, we demonstrate that this model learns
the relations between the facial parts and their distributions. Therefore, the
specific facial parts are interchangeable between generated face images
Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue
Effectively recognising and applying emotions to interactions is a highly
desirable trait for social robots. Implicitly understanding how subjects
experience different kinds of actions and objects in the world is crucial for
natural HRI interactions, with the possibility to perform positive actions and
avoid negative actions. In this paper, we utilize the NICO robot's appearance
and capabilities to give the NICO the ability to model a coherent affective
association between a perceived auditory stimulus and a temporally asynchronous
emotion expression. This is done by combining evaluations of emotional valence
from vision and language. NICO uses this information to make decisions about
when to extend conversations in order to accrue more affective information if
the representation of the association is not coherent. Our primary contribution
is providing a NICO robot with the ability to learn the affective associations
between a perceived auditory stimulus and an emotional expression. NICO is able
to do this for both individual subjects and specific stimuli, with the aid of
an emotion-driven dialogue system that rectifies emotional expression
incoherences. The robot is then able to use this information to determine a
subject's enjoyment of perceived auditory stimuli in a real HRI scenario