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@InProceedings{MartinsChiaFalc:2017:FaRoNe,
               author = "Martins, Samuel Botter and Chiachia, Giovani and Falc{\~a}o, 
                         Alexandre Xavier",
          affiliation = "{University of Campinas} and {University of Campinas} and 
                         {University of Campinas}",
                title = "A Fast and Robust Negative Mining Approach for Enrollment in Face 
                         Recognition Systems",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "face recognition, negative mining, convolutional networks.",
             abstract = "Consider a face image data set from clients of a company and the 
                         problem of building a face recognition system from it. Video 
                         cameras can be used to acquire several images per client in order 
                         to maximize the robustness of the system. However, as the data set 
                         grows huge, the accuracy of the system might be seriously 
                         compromised since the number of negative samples for each user is 
                         increasing. We propose here a first solution for this problem, 
                         which (i) limits the number of negative samples in the training 
                         set for preserving responsiveness during user enrollment, (ii) 
                         selects the most informative negative samples with respect to each 
                         user for preserving accuracy, and (iii) builds a user- specific 
                         classification model. We combine a high-dimensional data 
                         representation from deep learning with a method that selects 
                         negative samples from a large mining set and builds, within 
                         interactive times, effective user-specific training set and 
                         classifier, using linear support vector machines. The method can 
                         also be used with other feature extractors. It has shown superior 
                         performance as compared to five baseline methods on three 
                         unconstrained data sets.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "PID4954541.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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