@InProceedings{ZavanNascBellSilv:2016:CoLaMe,
               author = "Zavan, Fl{\'a}vio Henrique de Bittencourt and Nascimento, 
                         Ant{\^o}nio Carlos Paes and Bellon, Olga Regina Pereira and 
                         Silva, Luciano",
          affiliation = "{Universidade Federal do Paran{\'a}} and {Universidade Federal do 
                         Paran{\'a}} and {Universidade Federal do Paran{\'a}} and 
                         {Universidade Federal do Paran{\'a}}",
                title = "NosePose: a competitive, landmark-free methodology for head pose 
                         estimation in the wild",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Head pose estimation, Nose pose estimation, Face image analysis, 
                         Support vector machines, Convolutional neural network.",
             abstract = "We perform head pose estimation solely based on the nose region as 
                         input, extracted from 2D images in unconstrained environments. 
                         Such information is useful for many face analysis applications, 
                         such as recognition, reconstruction, alignment, tracking and 
                         expression recognition. Using the nose region has advantages over 
                         using the whole face; not only it is less likely to be occluded by 
                         acesssories, it is also visible and proved to be highly 
                         discriminant in all poses from profile to frontal. To this end, we 
                         propose and compare two different approaches, based on Support 
                         Vector Machines (SVM-NosePose) and on Convolutional Neural 
                         Networks (CNN-NosePose) such that no landmarks are needed to 
                         perform pose estimation, favoring success in extreme pose and 
                         environment where landmark detection is non-trivial. Our NosePose 
                         methodology was applied to four publicly available uncontrolled 
                         image datasets (McGillFaces, AFW, PaSC and IJB-A). Results show 
                         that both SVM-NosePose and CNN-NosePose approaches are 
                         competitive, through thoughtful and comprehensive experiments, 
                         when compared against state-of-the-art works on head pose 
                         estimation.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3ME7N65",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3ME7N65",
           targetfile = "nose_pose_camera_ready.pdf",
        urlaccessdate = "2024, May 03"
}