@InProceedings{PaixãoPereKoma:2017:ReExFa,
               author = "Paix{\~a}o, Wdnei Ribeiro da and Pereira, Fl{\'a}vio Garcia and 
                         Komati, Karin Satie",
          affiliation = "{Instituto Federal do Espirito Santo - Campus Serra} and 
                         {Instituto Federal do Espirito Santo - Campus Serra} and 
                         {Instituto Federal do Espirito Santo - Campus Serra}",
                title = "Reconhecimento de express{\~o}es faciais aplicada {\`a} 
                         an{\'a}lise de v{\'{\i}}deos com rea{\c{c}}{\~o}es 
                         espont{\^a}neas usando SVM com resposta probabil{\'{\i}}stica",
            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 = "reconhecimento de express{\~o}es faciais, rea{\c{c}}{\~o}es 
                         espont{\^a}neas, an{\'a}lise de v{\'{\i}}deos, machine 
                         learning, HoG, SVM.",
             abstract = "This work defines an initial proposal of a system for automated 
                         facial emotion classification applied to video that contains 
                         recordings of spontaneous reactions of spectators. The proposed 
                         approach uses a variation of the classifier Support Vector 
                         Machines (SVM) with outputs in a posteriori probability value and 
                         the Histogram of Oriented Gradients (HoG) as a feature descriptor. 
                         For the training, the Radboud Face Database (RaFD) was used. The 
                         results presented show the viability of the use in the mass media 
                         to assess the mood of the audience in quantitative terms of 
                         probability with respect to time.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3PJ4GM5",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PJ4GM5",
           targetfile = "7_wip.pdf",
        urlaccessdate = "2024, Apr. 29"
}