Ontology type: schema:ScholarlyArticle
2014-11
AUTHORSSofiane Boucenna, Philippe Gaussier, Pierre Andry, Laurence Hafemeister
ABSTRACTIn 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... »
PAGES633-652
http://scigraph.springernature.com/pub.10.1007/s12369-014-0245-z
DOIhttp://dx.doi.org/10.1007/s12369-014-0245-z
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1026608845
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
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