@InProceedings{CostaCoutCout:2019:FaReUs,
               author = "Costa, Murilo Villas Boas da and Couto, Cynthia Martins Villar and 
                         Couto, Leandro Nogueira",
          affiliation = "{Uberlandia Federal University} and {Sao Paulo University} and 
                         {Uberlandia Federal University}",
                title = "Face Recognition Using LBP on an Image Transformation Based on 
                         Complex Network Degrees",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "texture recognition, local binary patterns, complex networks.",
             abstract = "Automated visual face recognition involves acquiring descriptive 
                         features from the image. Local Binary Patterns (LBP) is a powerful 
                         method to that end, capably characterizing local features. An 
                         crucial limitation of LBP, however, is that the feature vector's 
                         size becomes unmanageable when the method employed on even 
                         moderately large regions. In order to describe larger scale 
                         features, this work proposes a descriptor based on applying the 
                         LBP histogram applied to an image transformation based on node 
                         degree data derived from a complex network representation of the 
                         original image. The complex network generation heuristic and 
                         parameters are discussed. The complex network representation is 
                         shown to be able to condense larger scale image patterns into a 
                         local value that can be handled by LBP. LBP applied to this image 
                         transformation yields results that outperform LBP. We validate our 
                         proposed approach by applying our method to a face recognition 
                         task using three challenging databases. Results demonstrate that, 
                         for a large enough complex network generation radius, our method 
                         consistently outperforms LBP, while using a feature vector of the 
                         same size.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00030",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00030",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U36MAB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U36MAB",
           targetfile = "
                         
                         Face_recognition_using_local_binary_patterns_on_an_image_transformation_based_on_complex_network_degrees___SIBGRAPI_2019.pdf",
        urlaccessdate = "2024, Apr. 28"
}