A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-11

AUTHORS

Sofiane Boucenna, Philippe Gaussier, Pierre Andry, Laurence Hafemeister

ABSTRACT

In this paper, we show that a robotic system can learn online to recognize facial expressions without having a teaching signal associating a facial expression with a given abstract label (e.g., ‘sadness’, ‘happiness’). Moreover, we show that recognizing a face from a non-face can be accomplished autonomously if we imagine that learning to recognize a face occurs after learning to recognize a facial expression, and not the opposite, as it is classically considered. In these experiments, the robot is considered as a baby because we want to understand how the baby can develop some abilities autonomously. We model, test and analyze cognitive abilities through robotic experiments. Our starting point was a mathematical model showing that, if the baby uses a sensory motor architecture for the recognition of a facial expression, then the parents must imitate the baby’s facial expression to allow the online learning. Here, a first series of robotic experiments shows that a simple neural network model can control a robot head and can learn online to recognize the facial expressions of the human partner if he/she imitates the robot’s prototypical facial expressions (the system is not using a model of the face nor a framing system). A second architecture using the rhythm of the interaction first allows a robust learning of the facial expressions without face tracking and next performs the learning involved in face recognition. Our more striking conclusion is that, for infants, learning to recognize a face could be more complex than recognizing a facial expression. Consequently, we emphasize the importance of the emotional resonance as a mechanism to ensure the dynamical coupling between individuals, allowing the learning of increasingly complex tasks. More... »

PAGES

633-652

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004-05. Robust Real-Time Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1996-06. Speed of processing in the human visual system in NATURE
  • 2010. Autonomous Development of Social Referencing Skills in FROM ANIMALS TO ANIMATS 11
  • 2004. Toward a Cognitive System Algebra: Application to Facial Expression Learning and Imitation in EMBODIED ARTIFICIAL INTELLIGENCE
  • 1989. Self-Organization and Associative Memory in NONE
  • 2005. Perception as a Dynamical Sensori-Motor Attraction Basin in ADVANCES IN ARTIFICIAL LIFE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z

    DOI

    http://dx.doi.org/10.1007/s12369-014-0245-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1026608845


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, CNRS UMR 8051, ENSEA, Cergy-Pontoise University, Cergy-Pontoise, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Boucenna", 
            "givenName": "Sofiane", 
            "id": "sg:person.07730435642.64", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07730435642.64"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, CNRS UMR 8051, ENSEA, Cergy-Pontoise University, Cergy-Pontoise, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gaussier", 
            "givenName": "Philippe", 
            "id": "sg:person.01041272554.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041272554.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, CNRS UMR 8051, ENSEA, Cergy-Pontoise University, Cergy-Pontoise, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Andry", 
            "givenName": "Pierre", 
            "id": "sg:person.012152621257.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152621257.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, CNRS UMR 8051, ENSEA, Cergy-Pontoise University, Cergy-Pontoise, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hafemeister", 
            "givenName": "Laurence", 
            "id": "sg:person.010726607664.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010726607664.26"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-88163-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001197038", 
              "https://doi.org/10.1007/978-3-642-88163-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-88163-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001197038", 
              "https://doi.org/10.1007/978-3-642-88163-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-27833-7_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001253955", 
              "https://doi.org/10.1007/978-3-540-27833-7_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:visi.0000013087.49260.fb", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001944608", 
              "https://doi.org/10.1023/b:visi.0000013087.49260.fb"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.imavis.2011.12.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005117427"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/1064546053278955", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006111495"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11553090_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007330833", 
              "https://doi.org/10.1007/11553090_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11553090_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007330833", 
              "https://doi.org/10.1007/11553090_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.patrec.2005.07.026", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007512787"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/088395198117596", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009321480"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1207/s15516709cog0901_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009400081"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1207/s15516709cog0901_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009400081"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1001/archneurpsyc.1937.02260220069003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016171111"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/381520a0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018357603", 
              "https://doi.org/10.1038/381520a0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.patrec.2005.04.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020433251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.patrec.2005.04.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020433251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.image.2004.05.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020547117"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1551-6708.1987.tb00862.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027181663"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1523/jneurosci.3403-09.2010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027208192"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0031-3203(96)00132-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030066254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15193-4_59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031351278", 
              "https://doi.org/10.1007/978-3-642-15193-4_59"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15193-4_59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031351278", 
              "https://doi.org/10.1007/978-3-642-15193-4_59"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09540090310001655110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037709041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0166-4115(97)80121-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038179048"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1040010953", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://app.dimensions.ai/details/publication/pub.1040010953", 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1753846.1754132", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043936993"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1754073910374662", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045962671"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1754073910374662", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045962671"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-2789(90)90087-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051758467"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-2789(90)90087-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051758467"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:visi.0000029664.99615.94", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052687286", 
              "https://doi.org/10.1023/b:visi.0000029664.99615.94"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/34.1000242", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061155594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/34.655647", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061156724"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/3468.952717", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061157851"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tamd.2009.2021702", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061488112"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tamd.2013.2284065", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061488222"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2002.1017616", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742389"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2008.52", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061743655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmca.2009.2033029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061795555"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tsmcb.2012.2193567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061797475"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3917/dev.012.0055", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1071595302"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.21236/ad0241531", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091822546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/devlrn.2009.5175536", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093186828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/fg.2011.5771357", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093289800"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ispa.2001.938703", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094366769"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2014-11", 
        "datePublishedReg": "2014-11-01", 
        "description": "In this paper, we show that a robotic system can learn online to recognize facial expressions without having a teaching signal associating a facial expression with a given abstract label (e.g., \u2018sadness\u2019, \u2018happiness\u2019). Moreover, we show that recognizing a face from a non-face can be accomplished autonomously if we imagine that learning to recognize a face occurs after learning to recognize a facial expression, and not the opposite, as it is classically considered. In these experiments, the robot is considered as a baby because we want to understand how the baby can develop some abilities autonomously. We model, test and analyze cognitive abilities through robotic experiments. Our starting point was a mathematical model showing that, if the baby uses a sensory motor architecture for the recognition of a facial expression, then the parents must imitate the baby\u2019s facial expression to allow the online learning. Here, a first series of robotic experiments shows that a simple neural network model can control a robot head and can learn online to recognize the facial expressions of the human partner if he/she imitates the robot\u2019s prototypical facial expressions (the system is not using a model of the face nor a framing system). A second architecture using the rhythm of the interaction first allows a robust learning of the facial expressions without face tracking and next performs the learning involved in face recognition. Our more striking conclusion is that, for infants, learning to recognize a face could be more complex than recognizing a facial expression. Consequently, we emphasize the importance of the emotional resonance as a mechanism to ensure the dynamical coupling between individuals, allowing the learning of increasingly complex tasks.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s12369-014-0245-z", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1049411", 
            "issn": [
              "1875-4791", 
              "1875-4805"
            ], 
            "name": "International Journal of Social Robotics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "6"
          }
        ], 
        "name": "A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game", 
        "pagination": "633-652", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7ec2ec90ac639c099341f7a4bcf0749a9763dbf210a27a04dfa19ce8cf240f4a"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12369-014-0245-z"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1026608845"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12369-014-0245-z", 
          "https://app.dimensions.ai/details/publication/pub.1026608845"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:11", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8678_00000522.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs12369-014-0245-z"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z'


     

    This table displays all metadata directly associated to this object as RDF triples.

    202 TRIPLES      21 PREDICATES      65 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12369-014-0245-z schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Na8b3895febea4b4b85b363754f4fe677
    4 schema:citation sg:pub.10.1007/11553090_5
    5 sg:pub.10.1007/978-3-540-27833-7_18
    6 sg:pub.10.1007/978-3-642-15193-4_59
    7 sg:pub.10.1007/978-3-642-88163-3
    8 sg:pub.10.1023/b:visi.0000013087.49260.fb
    9 sg:pub.10.1023/b:visi.0000029664.99615.94
    10 sg:pub.10.1038/381520a0
    11 https://app.dimensions.ai/details/publication/pub.1040010953
    12 https://doi.org/10.1001/archneurpsyc.1937.02260220069003
    13 https://doi.org/10.1016/0167-2789(90)90087-6
    14 https://doi.org/10.1016/j.image.2004.05.009
    15 https://doi.org/10.1016/j.imavis.2011.12.005
    16 https://doi.org/10.1016/j.patrec.2005.04.011
    17 https://doi.org/10.1016/j.patrec.2005.07.026
    18 https://doi.org/10.1016/s0031-3203(96)00132-x
    19 https://doi.org/10.1016/s0166-4115(97)80121-5
    20 https://doi.org/10.1080/088395198117596
    21 https://doi.org/10.1080/09540090310001655110
    22 https://doi.org/10.1109/34.1000242
    23 https://doi.org/10.1109/34.655647
    24 https://doi.org/10.1109/3468.952717
    25 https://doi.org/10.1109/devlrn.2009.5175536
    26 https://doi.org/10.1109/fg.2011.5771357
    27 https://doi.org/10.1109/ispa.2001.938703
    28 https://doi.org/10.1109/tamd.2009.2021702
    29 https://doi.org/10.1109/tamd.2013.2284065
    30 https://doi.org/10.1109/tpami.2002.1017616
    31 https://doi.org/10.1109/tpami.2008.52
    32 https://doi.org/10.1109/tsmca.2009.2033029
    33 https://doi.org/10.1109/tsmcb.2012.2193567
    34 https://doi.org/10.1111/j.1551-6708.1987.tb00862.x
    35 https://doi.org/10.1145/1753846.1754132
    36 https://doi.org/10.1162/1064546053278955
    37 https://doi.org/10.1177/1754073910374662
    38 https://doi.org/10.1207/s15516709cog0901_5
    39 https://doi.org/10.1523/jneurosci.3403-09.2010
    40 https://doi.org/10.21236/ad0241531
    41 https://doi.org/10.3917/dev.012.0055
    42 schema:datePublished 2014-11
    43 schema:datePublishedReg 2014-11-01
    44 schema:description In this paper, we show that a robotic system can learn online to recognize facial expressions without having a teaching signal associating a facial expression with a given abstract label (e.g., ‘sadness’, ‘happiness’). Moreover, we show that recognizing a face from a non-face can be accomplished autonomously if we imagine that learning to recognize a face occurs after learning to recognize a facial expression, and not the opposite, as it is classically considered. In these experiments, the robot is considered as a baby because we want to understand how the baby can develop some abilities autonomously. We model, test and analyze cognitive abilities through robotic experiments. Our starting point was a mathematical model showing that, if the baby uses a sensory motor architecture for the recognition of a facial expression, then the parents must imitate the baby’s facial expression to allow the online learning. Here, a first series of robotic experiments shows that a simple neural network model can control a robot head and can learn online to recognize the facial expressions of the human partner if he/she imitates the robot’s prototypical facial expressions (the system is not using a model of the face nor a framing system). A second architecture using the rhythm of the interaction first allows a robust learning of the facial expressions without face tracking and next performs the learning involved in face recognition. Our more striking conclusion is that, for infants, learning to recognize a face could be more complex than recognizing a facial expression. Consequently, we emphasize the importance of the emotional resonance as a mechanism to ensure the dynamical coupling between individuals, allowing the learning of increasingly complex tasks.
    45 schema:genre research_article
    46 schema:inLanguage en
    47 schema:isAccessibleForFree false
    48 schema:isPartOf N670e4ef31edf46448bc64cedd2c90343
    49 N8b23211967dd4038a100f89af6904e61
    50 sg:journal.1049411
    51 schema:name A Robot Learns the Facial Expressions Recognition and Face/Non-face Discrimination Through an Imitation Game
    52 schema:pagination 633-652
    53 schema:productId N6477c2dd319e4381bd4f3a3a121ea01b
    54 N7745a8ea6b41452780ebe5884c24d43d
    55 N84d21c3ecc2245c18dd393de7cac7b4c
    56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026608845
    57 https://doi.org/10.1007/s12369-014-0245-z
    58 schema:sdDatePublished 2019-04-10T19:11
    59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    60 schema:sdPublisher Nbd2806d205514e7ba5e9ddc08e061576
    61 schema:url http://link.springer.com/10.1007%2Fs12369-014-0245-z
    62 sgo:license sg:explorer/license/
    63 sgo:sdDataset articles
    64 rdf:type schema:ScholarlyArticle
    65 N1e7c43e15e644d79b2c38feae5d5543a rdf:first sg:person.012152621257.16
    66 rdf:rest Nf7d556a38f1645509f7177a0f98d74bb
    67 N6477c2dd319e4381bd4f3a3a121ea01b schema:name doi
    68 schema:value 10.1007/s12369-014-0245-z
    69 rdf:type schema:PropertyValue
    70 N670e4ef31edf46448bc64cedd2c90343 schema:volumeNumber 6
    71 rdf:type schema:PublicationVolume
    72 N7745a8ea6b41452780ebe5884c24d43d schema:name readcube_id
    73 schema:value 7ec2ec90ac639c099341f7a4bcf0749a9763dbf210a27a04dfa19ce8cf240f4a
    74 rdf:type schema:PropertyValue
    75 N84d21c3ecc2245c18dd393de7cac7b4c schema:name dimensions_id
    76 schema:value pub.1026608845
    77 rdf:type schema:PropertyValue
    78 N8b23211967dd4038a100f89af6904e61 schema:issueNumber 4
    79 rdf:type schema:PublicationIssue
    80 N91f38d92d99241cdbc323dd748882d38 rdf:first sg:person.01041272554.05
    81 rdf:rest N1e7c43e15e644d79b2c38feae5d5543a
    82 Na8b3895febea4b4b85b363754f4fe677 rdf:first sg:person.07730435642.64
    83 rdf:rest N91f38d92d99241cdbc323dd748882d38
    84 Nbd2806d205514e7ba5e9ddc08e061576 schema:name Springer Nature - SN SciGraph project
    85 rdf:type schema:Organization
    86 Nf7d556a38f1645509f7177a0f98d74bb rdf:first sg:person.010726607664.26
    87 rdf:rest rdf:nil
    88 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    89 schema:name Information and Computing Sciences
    90 rdf:type schema:DefinedTerm
    91 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    92 schema:name Artificial Intelligence and Image Processing
    93 rdf:type schema:DefinedTerm
    94 sg:journal.1049411 schema:issn 1875-4791
    95 1875-4805
    96 schema:name International Journal of Social Robotics
    97 rdf:type schema:Periodical
    98 sg:person.01041272554.05 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    99 schema:familyName Gaussier
    100 schema:givenName Philippe
    101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041272554.05
    102 rdf:type schema:Person
    103 sg:person.010726607664.26 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    104 schema:familyName Hafemeister
    105 schema:givenName Laurence
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010726607664.26
    107 rdf:type schema:Person
    108 sg:person.012152621257.16 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    109 schema:familyName Andry
    110 schema:givenName Pierre
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152621257.16
    112 rdf:type schema:Person
    113 sg:person.07730435642.64 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    114 schema:familyName Boucenna
    115 schema:givenName Sofiane
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07730435642.64
    117 rdf:type schema:Person
    118 sg:pub.10.1007/11553090_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007330833
    119 https://doi.org/10.1007/11553090_5
    120 rdf:type schema:CreativeWork
    121 sg:pub.10.1007/978-3-540-27833-7_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001253955
    122 https://doi.org/10.1007/978-3-540-27833-7_18
    123 rdf:type schema:CreativeWork
    124 sg:pub.10.1007/978-3-642-15193-4_59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031351278
    125 https://doi.org/10.1007/978-3-642-15193-4_59
    126 rdf:type schema:CreativeWork
    127 sg:pub.10.1007/978-3-642-88163-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001197038
    128 https://doi.org/10.1007/978-3-642-88163-3
    129 rdf:type schema:CreativeWork
    130 sg:pub.10.1023/b:visi.0000013087.49260.fb schema:sameAs https://app.dimensions.ai/details/publication/pub.1001944608
    131 https://doi.org/10.1023/b:visi.0000013087.49260.fb
    132 rdf:type schema:CreativeWork
    133 sg:pub.10.1023/b:visi.0000029664.99615.94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052687286
    134 https://doi.org/10.1023/b:visi.0000029664.99615.94
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1038/381520a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018357603
    137 https://doi.org/10.1038/381520a0
    138 rdf:type schema:CreativeWork
    139 https://app.dimensions.ai/details/publication/pub.1040010953 schema:CreativeWork
    140 https://doi.org/10.1001/archneurpsyc.1937.02260220069003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016171111
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/0167-2789(90)90087-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051758467
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/j.image.2004.05.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020547117
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/j.imavis.2011.12.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005117427
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/j.patrec.2005.04.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020433251
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/j.patrec.2005.07.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007512787
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/s0031-3203(96)00132-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1030066254
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/s0166-4115(97)80121-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038179048
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1080/088395198117596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009321480
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1080/09540090310001655110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037709041
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1109/34.1000242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061155594
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1109/34.655647 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156724
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1109/3468.952717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157851
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1109/devlrn.2009.5175536 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093186828
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1109/fg.2011.5771357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093289800
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1109/ispa.2001.938703 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094366769
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1109/tamd.2009.2021702 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061488112
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1109/tamd.2013.2284065 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061488222
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1109/tpami.2002.1017616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742389
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1109/tpami.2008.52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743655
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1109/tsmca.2009.2033029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061795555
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1109/tsmcb.2012.2193567 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061797475
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1111/j.1551-6708.1987.tb00862.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027181663
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1145/1753846.1754132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043936993
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1162/1064546053278955 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006111495
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1177/1754073910374662 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045962671
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1207/s15516709cog0901_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009400081
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1523/jneurosci.3403-09.2010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027208192
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.21236/ad0241531 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091822546
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.3917/dev.012.0055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071595302
    199 rdf:type schema:CreativeWork
    200 https://www.grid.ac/institutes/grid.463844.9 schema:alternateName Information Processing and System Research Lab
    201 schema:name ETIS, CNRS UMR 8051, ENSEA, Cergy-Pontoise University, Cergy-Pontoise, France
    202 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...