@InProceedings{ZavanSilvBell:2017:NoPoEs,
               author = "Zavan, Fl{\'a}vio Henrique de Bittencourt and Silva, Luciano and 
                         Bellon, Olga Regina Pereira",
          affiliation = "{Universidade Federal do Paran{\'a}} and {Universidade Federal do 
                         Paran{\'a}} and {Universidade Federal do Paran{\'a}}",
                title = "Nose pose estimation in the wild and its applications on nose 
                         tracking and 3D face alignment",
            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 = "face processing, face analysis, head pose estimation.",
             abstract = "An automatic, landmark free SVM-based method for head pose 
                         estimation, solely using the nose region, in constrained and 
                         unconstrained scenarios, is presented. Using the nose region has 
                         advantages over the whole face; it is less likely to be occluded 
                         or deformed by facial expressions, and is proven to be highly 
                         discriminant in all poses from profile to frontal. The approach, 
                         SVM-NosePose, receives a nose region as and classifies it into a 
                         discrete set of poses. Estimation favorably compares against 
                         state-of-the-art works on six publicly available datasets. Three 
                         applications are derived from the proposed methodology: 1) the 
                         original inclusion of a head pose score for face quality 
                         estimation for initializing a nose tracker, leading to higher 
                         accuracy; 2) 3D face alignment in the wild using only the nose 
                         pose, enabling consistent estimates even in challenging scenarios; 
                         and 3) multipose action unit detection and intensity estimation 
                         for facial images in the wild.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PK84H8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PK84H8",
           targetfile = "wtd_sibgrapi_2017_camera_ready.pdf",
        urlaccessdate = "2024, May 02"
}