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

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 N5a48cc2fa67448498f1eb1b2d744cd6c
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 N32328955ca59440ba41fe897f3e126e1
49 N882df527a0e445f4a120247708eaa2f0
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 N05a69144d39e4ac3a019124ce1c56481
54 Na28aa3d44257459eb3cfd6c5d33291b7
55 Ne20a0757f3294c0486823bcfe5a19e06
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 N983482bd7f1d472ab5a64d9b21a716a0
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 N05a69144d39e4ac3a019124ce1c56481 schema:name readcube_id
66 schema:value 7ec2ec90ac639c099341f7a4bcf0749a9763dbf210a27a04dfa19ce8cf240f4a
67 rdf:type schema:PropertyValue
68 N2a12e53c52be424f8e795a3df5bbfa09 rdf:first sg:person.010726607664.26
69 rdf:rest rdf:nil
70 N32328955ca59440ba41fe897f3e126e1 schema:issueNumber 4
71 rdf:type schema:PublicationIssue
72 N5a48cc2fa67448498f1eb1b2d744cd6c rdf:first sg:person.07730435642.64
73 rdf:rest Nc0cbc6aaeba04c41a91e48e994fb20f1
74 N882df527a0e445f4a120247708eaa2f0 schema:volumeNumber 6
75 rdf:type schema:PublicationVolume
76 N983482bd7f1d472ab5a64d9b21a716a0 schema:name Springer Nature - SN SciGraph project
77 rdf:type schema:Organization
78 N9c8109df04ff4bdc994744726c8628ca rdf:first sg:person.012152621257.16
79 rdf:rest N2a12e53c52be424f8e795a3df5bbfa09
80 Na28aa3d44257459eb3cfd6c5d33291b7 schema:name dimensions_id
81 schema:value pub.1026608845
82 rdf:type schema:PropertyValue
83 Nc0cbc6aaeba04c41a91e48e994fb20f1 rdf:first sg:person.01041272554.05
84 rdf:rest N9c8109df04ff4bdc994744726c8628ca
85 Ne20a0757f3294c0486823bcfe5a19e06 schema:name doi
86 schema:value 10.1007/s12369-014-0245-z
87 rdf:type schema:PropertyValue
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)


...