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@InProceedings{BezerraGome:2018:ReOcLa,
               author = "Bezerra, Gustavo Alves and Gomes, Rafael Beserra",
          affiliation = "{Universidade Federal do Rio Grande do Norte} and {Universidade 
                         Federal do Rio Grande do Norte}",
                title = "Recognition of occluded and lateral faces using MTCNN, Dlib and 
                         homographies",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "face recognition, occlusion, homography.",
             abstract = "With the advance of technology it is possible to create more 
                         robust security systems. For this task, image processing alongside 
                         Deep Neural Networks are currently being used in several works for 
                         facial recognition. However, occlusions and faces in different 
                         angles are a challenge for most algorithms. Attempting to contour 
                         this issue, an algorithm for facial recognition combining MTCNN, 
                         DLIB and homographies is proposed. In the obtained results, a 
                         comparison between the proposed algorithm and basis works 
                         indicates that, for some controlled cases, a mean accuracy 
                         improvement of 7.4% was obtained, with a maximum of 8.23% for 
                         occluded faces and 14.08% for lateral faces.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S39KCL",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3S39KCL",
           targetfile = "Recognition_of_Occluded_and_Lateral_Faces.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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