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@InProceedings{Jung:2018:ReGeDe,
               author = "zeni, luis felipe de araujo and Jung, Claudio Rosito",
          affiliation = "{universidade federal do rio grande do sul} and {universidade 
                         federal do rio grande do sul}",
                title = "Real-Time Gender Detection in the Wild Using Deep Neural 
                         Networks",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep learning, computer vision, gender detection, real-time.",
             abstract = "Gender recognition can be used in many applications, such as video 
                         surveillance, human-computer interaction and customized 
                         advertisement. Current state-of-the-art gender recognition methods 
                         are detector-dependent or region-dependent, focusing mostly on 
                         facial features (a face detector is typically required). These 
                         limitations do not allow an end-to-end training pipeline, and many 
                         features used in the detection phase must be re-learned in the 
                         classification step. Furthermore, the use of facial features 
                         limits the application of such methods in the wild, where the face 
                         might not be present. This paper presents a real-time end-to-end 
                         gender detector based on deep neural networks. The proposed method 
                         detects and recognizes the gender of persons in the wild, meaning 
                         in images with a high variability in pose, illumination an 
                         occlusions. To train and evaluate the results a new annotation set 
                         of Pascal VOC 2007 and CelebA were created. Our experimental 
                         results indicate that combining both datasets during training can 
                         increase the mAp of our gender detector. We also visually analyze 
                         which parts leads our network to make mistakes and the bias 
                         introduced by the training data.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "87.pdf",
        urlaccessdate = "2020, Dec. 02"
}


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