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@InProceedings{MouraGomeCarv:2013:ImFaVe,
               author = "Moura, Eduardo Santiago and Gomes, Herman Martins and de Carvalho, 
                         Jo{\~a}o Marques",
          affiliation = "{Federal University of Campina Grande} and {Federal University of 
                         Campina Grande} and {Federal University of Campina Grande}",
                title = "An Improved Face Verification Approach based on Speedup Robust 
                         Features and Pairwise Matching",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "speedup robust features, pairwise matching, face verification, 
                         Labeled Faces in the Wild, unsupervised protocol.",
             abstract = "Human faces are known to present large variability due to factors 
                         like pose and facial expression variations, changes in 
                         illumination and occlusion, among others, thus making face 
                         verification a very challenging problem. In this paper we address 
                         the problem of face verification with special interest on how to 
                         reduce degradation usually associated with face images acquired 
                         under uncontrolled environments. The approach we propose in this 
                         paper starts with a preprocessing step to correct in-plane face 
                         orientation and to compensate for illumination changes. SURF 
                         features are then extracted, which adds to the method a certain 
                         degree of invariance to pose, facial expression and other sources 
                         of variation. Taking the SURF features as input, an original 
                         pairwise face matching procedure is performed. The resulting 
                         matching scores are stored in a similarity matrix, which is then 
                         evaluated. An experimental study has revealed that the proposed 
                         approach produced the best ROC curve when compared to published 
                         work regarding the unsupervised setup of the Labeled Faces in the 
                         Wild (LFW) face database.",
  conference-location = "Arequipa, Peru",
      conference-year = "Aug. 5-8, 2013",
             language = "en",
           targetfile = "MouraGomesCarvalhov2.0 (1).pdf",
        urlaccessdate = "2020, Dec. 05"
}


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