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@InProceedings{KuboBellFlynSilv:2017:CoFeEx,
               author = "Kubo, Diandra Akemi Alves and Bellon, Olga Regina Pereira and 
                         Flynn, Patrick and Silva, Luciano",
          affiliation = "{Universidade Federal do Paran{\'a}} and {Universidade Federal do 
                         Paran{\'a}} and {University of Notre Dame} and {Universidade 
                         Federal do Paran{\'a}}",
                title = "Facial Expression Recognition: Comparison of Feature Extraction 
                         Methods",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "facial expression, feature extraction.",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJD3L2",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJD3L2",
           targetfile = "2017_sibgrapi_daakubo.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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