@InProceedings{VaretoSilvCostSchw:2017:FaVeBa,
               author = "Vareto, Rafael Henrique and Silva, Samira Santos da and Costa, 
                         Filipe de Oliveira and Schwartz, William Robson",
          affiliation = "{Universidade Federak de Minas Gerais} and {Universidade Federak 
                         de Minas Gerais} and {Universidade Federak de Minas Gerais} and 
                         {Universidade Federak de Minas Gerais}",
                title = "Face Verification based on Relational Disparity Features and 
                         Partial Least Squares Models",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Face verification, partial least squares, relational features.",
             abstract = "Face verification approaches aim at determining whether two given 
                         faces are from the same person. This scenario has several 
                         applications, such as information security, forensics, 
                         surveillance and smart cards. Several works extract features 
                         independently from each face image, i.e., any sort of relation 
                         between the two faces is not modeled a priori to either training 
                         or classification stages. In this work, we propose an approach 
                         that compares a pair of faces by extracting relational features, 
                         assuming the hypothesis that modeling the relation between two 
                         faces can be useful for increasing the robustness and performance 
                         of the face verification task. Then, we employ multiple 
                         classification models based on Partial Least Squares to verify 
                         whether a given pair of images belongs the same subject (genuine) 
                         or belongs to different subjects (impostor). We validate our 
                         approach on the Labeled Faces in the Wild (LFW) and on the Public 
                         Figures (Pubfig) datasets, using only few images for training. 
                         According to the experiments, our approach achieves results up to 
                         0.966 of area under the curve (AUC) for the LFW dataset using its 
                         unrestricted, labeled outside data protocol and an average equal 
                         error (EER) of 13.65% on PubFig dataset.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.34",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.34",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFCA92",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFCA92",
           targetfile = "PID4957039.pdf",
        urlaccessdate = "2024, May 01"
}