@InProceedings{SilvaCostSchw:2018:AgPaLe,
               author = "Silva, Samira and Costa, Filipe and Schwartz, William Robson",
          affiliation = "{Federal University of Minas Gerais} and {CPqD - Image and Speech 
                         Processing Management} and {Federal University of Minas Gerais}",
                title = "Aggregating Partial Least Squares Models for Open-set Face 
                         Identification",
            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 = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Open-set Face Recognition, Face Identification, Partial Least 
                         Squares.",
             abstract = "Face identification is an important task in computer vision and 
                         has a myriad of applications, such as in surveillance, forensics 
                         and human-computer interaction. In the past few years, several 
                         methods have been proposed to solve face identification task in 
                         closed-set scenarios, that is, methods that make assumption of all 
                         the probe images necessarily matching a gallery individual. 
                         However, in real-world applications, one might want to determine 
                         the identity of an unknown face in open-set scenarios. In this 
                         work, we propose a novel method to perform open-set face 
                         identification by aggregating Partial Least Squares models using 
                         the one-against-all protocol in a simple but fast way. The model 
                         outputs are combined into a response histogram which is balanced 
                         if the probe face belongs to a gallery individual or have a 
                         highlighted bin, otherwise. Evaluation is performed in four 
                         datasets: FRGCv1, FG-NET, Pubfig and Pubfig83. Results show 
                         significant improvement when compared to state-of-the art 
                         approaches regardless challenges posed by different datasets.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
             language = "en",
                  ibi = "8JMKD3MGPAW/3S396AB",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3S396AB",
           targetfile = "2018-wtd26-samira-silva_camera-ready.pdf",
        urlaccessdate = "2024, May 19"
}