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@InProceedings{LeitePeGoVeSaNaCa:2007:LeEyDe,
               author = "Leite, Bruno de Brito and Pereira, Eanes Torres and Gomes, Herman 
                         Martins and Veloso, Luciana Ribeiro and Santos, Cī\ı and 
                         Nascimento, cero Einstein do and de Carvalho, Jo{\~a}o Marques",
          affiliation = "Departamento de Sistemas e Computa{\c{c}}{\~a}o, Universidade 
                         Federal de Campina Grande and Departamento de Sistemas e 
                         Computa{\c{c}}{\~a}o, Universidade Federal de Campina Grande and 
                         Departamento de Sistemas e Computa{\c{c}}{\~a}o, Universidade 
                         Federal de Campina Grande and Departamento de Engenharia 
                         El{\'e}trica, Universidade Federal de Campina Grande and 
                         Departamento de Engenharia El{\'e}trica, Universidade Federal de 
                         Campina Grande and Departamento de Engenharia El{\'e}trica, 
                         Universidade Federal de Campina Grande",
                title = "A learning-based eye detector coupled with eye candidate filtering 
                         and PCA features",
            booktitle = "Proceedings...",
                 year = "2007",
               editor = "Falc{\~a}o, Alexandre Xavier and Lopes, H{\'e}lio C{\^o}rtes 
                         Vieira",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "eye detection, neural networks, principal component analysis, 
                         integral image.",
             abstract = "In this work, we present a system based on a Neural Network 
                         classifier for eye detection in human face images. This classifier 
                         works on eye candidate regions extracted from a face image and 
                         represented by a reduced number of features, selected by Principal 
                         Component Analysis. The regions are determined considering that in 
                         an image window containing the eye, the gray level distribution 
                         will generally assume a pattern of adjacent light-dark-light 
                         horizontal and vertical stripes, corresponding to the eyelid, 
                         pupil and eyelid, respectively. For training, validation and 
                         testing, a database was built with a total of 4,400 images. 
                         Experimental results have shown that the proposed approach 
                         correctly detects more eyes than any of two existing systems 
                         (Rowley-Baluja-Kanade and Machine Perception Toolbox), for eye 
                         location error tolerances from 0 to 5 pixels. Considering an error 
                         tolerance of 9 pixels, the correct detection rate achieved was 
                         above 90%. .",
  conference-location = "Belo Horizonte",
      conference-year = "Oct. 7-10, 2007",
             language = "en",
           targetfile = "gomes-EyeDetection.pdf",
        urlaccessdate = "2020, Oct. 22"
}


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