Journal article Open Access

Affective Robots: Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines

Silvia Santano Guillén; Luigi Lo Iacono; Christian Meder


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    <subfield code="a">One of the main aims of current social robotic research&lt;br&gt;
is to improve the robots&amp;rsquo; abilities to interact with humans. In order&lt;br&gt;
to achieve an interaction similar to that among humans, robots&lt;br&gt;
should be able to communicate in an intuitive and natural way&lt;br&gt;
and appropriately interpret human affects during social interactions.&lt;br&gt;
Similarly to how humans are able to recognize emotions in other&lt;br&gt;
humans, machines are capable of extracting information from the&lt;br&gt;
various ways humans convey emotions&amp;mdash;including facial expression,&lt;br&gt;
speech, gesture or text&amp;mdash;and using this information for improved&lt;br&gt;
human computer interaction. This can be described as Affective&lt;br&gt;
Computing, an interdisciplinary field that expands into otherwise&lt;br&gt;
unrelated fields like psychology and cognitive science and involves&lt;br&gt;
the research and development of systems that can recognize and&lt;br&gt;
interpret human affects. To leverage these emotional capabilities&lt;br&gt;
by embedding them in humanoid robots is the foundation of&lt;br&gt;
the concept Affective Robots, which has the objective of making&lt;br&gt;
robots capable of sensing the user&amp;rsquo;s current mood and personality&lt;br&gt;
traits and adapt their behavior in the most appropriate manner&lt;br&gt;
based on that. In this paper, the emotion recognition capabilities&lt;br&gt;
of the humanoid robot Pepper are experimentally explored, based&lt;br&gt;
on the facial expressions for the so-called basic emotions, as&lt;br&gt;
well as how it performs in contrast to other state-of-the-art&lt;br&gt;
approaches with both expression databases compiled in academic&lt;br&gt;
environments and real subjects showing posed expressions as well&lt;br&gt;
as spontaneous emotional reactions. The experiments&amp;rsquo; results show&lt;br&gt;
that the detection accuracy amongst the evaluated approaches differs&lt;br&gt;
substantially. The introduced experiments offer a general structure&lt;br&gt;
and approach for conducting such experimental evaluations. The&lt;br&gt;
paper further suggests that the most meaningful results are obtained&lt;br&gt;
by conducting experiments with real subjects expressing the emotions&lt;br&gt;
as spontaneous reactions.</subfield>
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