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  <subfield code="a">LEEDS09a</subfield> 
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<subfield code="p">TRANSP-OR</subfield>
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<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
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 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
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<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Automatic facial expression recognition: a discrete choice approach</subfield>
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<datafield tag="260" ind1="" ind2="">
<subfield code="c">2009</subfield>
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<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
International Choice Modelling Conference</subfield>
<subfield code="c">
Institute for Transport Studies at the University of Leeds, Harrogate, UK</subfield>
<subfield code="d">April 01, 2009</subfield>
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<datafield tag="520" ind1="" ind2="">
<subfield code="a">
Automatic facial expression recognition finds applications in various fields where human-machine interactions are involved.  We propose a framework based on discrete choice models, where we try to forecast how a human person would evaluate the facial expression, choosing the most appropriate label among a given list. After having applied the framework successfully on static images, we investigate the possibility to apply it on video sequences.

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