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		<citationkey>KuboBellFlynSilv:2017:CoFeEx</citationkey>
		<title>Facial Expression Recognition: Comparison of Feature Extraction Methods</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>1338 KiB</size>
		<author>Kubo, Diandra Akemi Alves,</author>
		<author>Bellon, Olga Regina Pereira,</author>
		<author>Flynn, Patrick,</author>
		<author>Silva, Luciano,</author>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<affiliation>University of Notre Dame</affiliation>
		<affiliation>Universidade Federal do Paraná</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>diandraakemi@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Undergraduate Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>facial expression, feature extraction.</keywords>
		<abstract>Human  facial  expressions  are  one  of  the  most  im-portant communication channels, being used with trust to betterunderstand  ones  state  of  mind  in  a  variety  of  applications,  forinstance, emotion recognition. As a result, various algorithms andmethods  have  been  developed  for  facial  expression  recognition.On  this  context,  we  review  the  literature  and  conduct  tests  ondifferent algorithms regarding facial feature extraction, in orderto  evaluate  their  performance  on  the  BU-3DFE  database.  Thisdatabase  was  chosen  because  it  is  widely  used  and  all  emotionsare annotated for each image. Therefore BU-3DFE is suitable forthe  proposed  benchmarking.  The  best  result  was  achieved  by  acombination  of  Eigenfaces  and  SVM  as  classifier.</abstract>
		<language>en</language>
		<targetfile>2017_sibgrapi_daakubo.pdf</targetfile>
		<usergroup>diandraakemi@gmail.com</usergroup>
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