@InProceedings{ZavarezBerrOliv:2017:CrFaEx,
               author = "Zavarez, Marcus Vinicius and Berriel, Rodrigo F. and 
                         Oliveira-Santos, Thiago",
          affiliation = "{Universidade Federal do Espirito Santo} and {Universidade Federal 
                         do Espirito Santo} and {Universidade Federal do Espirito Santo}",
                title = "Cross-Database Facial Expression Recognition Based on Fine-Tuned 
                         Deep Convolutional Network",
            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 = "Facial expression recognition, convolutional neural network, deep 
                         learning, cross-database.",
             abstract = "Facial expression recognition is a very important research field 
                         to understand human emotions. Many facial expression recognition 
                         systems have been proposed in the literature over the years. Some 
                         of these methods use neural network approaches with deep 
                         architectures to address the problem. Although it seems that the 
                         facial expression recognition problem has been solved, there is a 
                         large difference between the results achieved using the same 
                         database to train and test the network and the cross-database 
                         protocol. In this paper, we extensively investigate the 
                         performance influence of fine-tuning with cross-database approach. 
                         In order to perform the study, the VGG-Face Deep Convolutional 
                         Network model (pre-trained for face recognition) was fine-tuned to 
                         recognize facial expressions considering different 
                         well-established databases in the literature: CK+, JAFFE, MMI, 
                         RaFD, KDEF, BU3DFE, and AR Face. The cross-database experiments 
                         were organized so that one of the databases was separated as test 
                         set and the others as training, and each experiment was ran 
                         multiple times to ensure the results. Our results show a 
                         significant improvement on the use of pre-trained models against 
                         randomly initialized Convolutional Neural Networks on the facial 
                         expression recognition problem, for example achieving 88.58%, 
                         67.03%, 85.97%, and 72.55% average accuracy testing in the CK+, 
                         MMI, RaFD, and KDEF, respectively. Additionally, in absolute 
                         terms, the results show an improvement in the literature for 
                         cross-database facial expression recognition with the use of 
                         pre-trained models.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.60",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.60",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PFBDPL",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFBDPL",
           targetfile = "PaperSib2017.pdf",
        urlaccessdate = "2024, Apr. 27"
}