@InProceedings{BatistaBellSilv:2016:LaSmIn,
               author = "Batista, J{\'u}lio C{\'e}sar 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}}",
                title = "Landmark-free smile intensity estimation",
            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 = "smile intensity estimation, facial expression analysis, feature 
                         extraction, machine learning.",
             abstract = "Facial expression analysis is an important field of research, 
                         mostly because of the rich information faces can provide. The 
                         majority of works published in the literature have focused on 
                         facial expression recognition and so far estimating facial 
                         expression intensities have not gathered same attention. The 
                         analysis of these intensities could improve face processing 
                         applications on distinct areas, such as computer assisted health 
                         care, human-computer interaction and biometrics. Because the smile 
                         is the most common expression, studying its intensity is a first 
                         step towards estimating other expressions intensities. Most 
                         related works are based on facial landmarks, sometimes combined 
                         with appearance features around these points, to estimate smile 
                         intensities. Relying on landmarks can lead to wrong estimations 
                         due to errors in the registration step. In this work we 
                         investigate a landmark-free approach for smile intensity 
                         estimation using appearance features from a grid division of the 
                         face. We tested our approach on two different databases, one with 
                         spontaneous expressions (BP4D) and the other with posed 
                         expressions (BU-3DFE); results are compared to state-of-the-art 
                         works in the field. Our method shows competitive results even 
                         using only appearance features on spontaneous facial expression 
                         intensities, but we found that there is still need for further 
                         investigation on posed expressions.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3ME7NF2",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3ME7NF2",
           targetfile = "Landmark_free_smile_intensity_estimation.pdf",
        urlaccessdate = "2024, Apr. 28"
}