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@InProceedings{LopesAguiOliv:2015:FaExRe,
               author = "Lopes, Andre Teixeira and Aguiar, Edilson de and Oliveira-Santos, 
                         Thiago",
          affiliation = "{Universidade Federal do Esp{\'{\i}}rito Santo} and 
                         {Universidade Federal do Esp{\'{\i}}rito Santo} and 
                         {Universidade Federal do Esp{\'{\i}}rito Santo}",
                title = "A Facial Expression Recognition System Using Convolutional 
                         Networks",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Expression, Convolutional Networks, Computer Vision, Machine 
                         Learning, Expression Specific Features.",
             abstract = "Facial expression recognition has been an active research area in 
                         the past ten years, with a growing application area like avatar 
                         animation and neuromarketing. The recognition of facial 
                         expressions is not an easy problem for machine learning methods, 
                         since different people can vary in the way that they show their 
                         expressions. And even an image of the same person in one 
                         expression can vary in brightness, background and position. 
                         Therefore, facial expression recognition is still a challenging 
                         problem in computer vision. In this work, we propose a simple 
                         solution for facial expression recognition that uses a combination 
                         of standard methods, like Convolutional Network and specific image 
                         pre-processing steps. Convolutional networks, and the most machine 
                         learning methods, achieve better accuracy depending on a given 
                         feature set. Therefore, a study of some image pre-processing 
                         operations that extract only expression specific features of a 
                         face image is also presented. The experiments were carried out 
                         using a largely used public database for this problem. A study of 
                         the impact of each image pre-processing operation in the accuracy 
                         rate is presented. To the best of our knowledge, our method 
                         achieves the best result in the literature, 97.81% of accuracy, 
                         and takes less time to train than state-of-the-art methods.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3755347.pdf",
        urlaccessdate = "2021, Dec. 03"
}


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