@InProceedings{SilvaZavaBellSilv:2016:TrFaBa,
               author = "Silva, Luan Porf{\'{\i}}rio e and Zavan, Fl{\'a}vio Henrique de 
                         Bittencourt 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 = "Follow that nose: tracking faces based on the nose region and 
                         image quality feedback",
            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 = "Face tracker, Nose region, Face image quality.",
             abstract = "Face tracking uses temporal information to infer the position of 
                         the face in each frame. One of its applications is in 
                         unconstrained (in-the-wild) environments where face detection 
                         methods fail to perform robustly. Current approaches presented in 
                         the literature are based on facial landmarks. Therefore, they have 
                         limitations when applied in in-the-wild environments as estimating 
                         the landmarks in such scenarios is not trivial. To address this 
                         issue, we propose a novel landmark-free approach based on a 
                         state-of-the-art generic visual tracking method, as baseline, 
                         combined with face quality assessment for initializing the 
                         tracking. In addition, we introduce using only the nose region as 
                         a solution for in-the-wild face tracking, initializing it with the 
                         nose of the best quality face in the video sequence. The nose is 
                         detected and used to estimate the head pose, which is combined 
                         with the face quality score for choosing the initialization frame. 
                         The nose region, rather than the entire face was chosen due to it 
                         being unlikely to be occluded, mostly invariant to facial 
                         expressions and visible in a long range of head poses. We 
                         performed experiments on the 300 Videos in the Wild dataset and 
                         our results favorably compared against the baseline method.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3ME7PA8",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3ME7PA8",
           targetfile = "Follow_Sibgrapi_Noses.pdf",
        urlaccessdate = "2024, May 03"
}