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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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "One of the main aims of current social robotic research<br>\nis to improve the robots&rsquo; abilities to interact with humans. In order<br>\nto achieve an interaction similar to that among humans, robots<br>\nshould be able to communicate in an intuitive and natural way<br>\nand appropriately interpret human affects during social interactions.<br>\nSimilarly to how humans are able to recognize emotions in other<br>\nhumans, machines are capable of extracting information from the<br>\nvarious ways humans convey emotions&mdash;including facial expression,<br>\nspeech, gesture or text&mdash;and using this information for improved<br>\nhuman computer interaction. This can be described as Affective<br>\nComputing, an interdisciplinary field that expands into otherwise<br>\nunrelated fields like psychology and cognitive science and involves<br>\nthe research and development of systems that can recognize and<br>\ninterpret human affects. To leverage these emotional capabilities<br>\nby embedding them in humanoid robots is the foundation of<br>\nthe concept Affective Robots, which has the objective of making<br>\nrobots capable of sensing the user&rsquo;s current mood and personality<br>\ntraits and adapt their behavior in the most appropriate manner<br>\nbased on that. In this paper, the emotion recognition capabilities<br>\nof the humanoid robot Pepper are experimentally explored, based<br>\non the facial expressions for the so-called basic emotions, as<br>\nwell as how it performs in contrast to other state-of-the-art<br>\napproaches with both expression databases compiled in academic<br>\nenvironments and real subjects showing posed expressions as well<br>\nas spontaneous emotional reactions. The experiments&rsquo; results show<br>\nthat the detection accuracy amongst the evaluated approaches differs<br>\nsubstantially. The introduced experiments offer a general structure<br>\nand approach for conducting such experimental evaluations. The<br>\npaper further suggests that the most meaningful results are obtained<br>\nby conducting experiments with real subjects expressing the emotions<br>\nas spontaneous reactions.", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Silvia Santano Guill\u00e9n"
    }, 
    {
      "@type": "Person", 
      "name": "Luigi Lo Iacono"
    }, 
    {
      "@type": "Person", 
      "name": "Christian Meder"
    }
  ], 
  "headline": "Affective Robots: Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-04-04", 
  "url": "https://zenodo.org/record/1316752", 
  "version": "10009027", 
  "keywords": [
    "Affective computing", 
    "emotion recognition", 
    "humanoid\nrobot", 
    "Human-Robot-Interaction (HRI)", 
    "social robots."
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1316752", 
  "@id": "https://doi.org/10.5281/zenodo.1316752", 
  "@type": "ScholarlyArticle", 
  "name": "Affective Robots: Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines"
}
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