@InProceedings{SantosJrSchw:2016:PaLeSq,
               author = "Santos Junior, Cassio E dos and Schwartz, William Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais}",
                title = "Partial Least Squares for Face Hashing",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "face identification, partial least squares, large-scale image 
                         retrieval.",
             abstract = "Face identification is an important research topic for 
                         applications such as surveillance, forensics, and human-computer 
                         interaction. In the past few years, a myriad of methods for face 
                         identification has been proposed in the literature, with just a 
                         few among them focusing on scalability. In this work, we propose a 
                         simple but efficient approach for scalable face identification 
                         based on partial least squares (PLS) and random independent hash 
                         functions inspired by locality-sensitive hashing (LSH), resulting 
                         in the PLS for hashing (PLSH) approach. The original PLSH approach 
                         is further extended using feature selection to reduce the 
                         computational cost to evaluate the PLS- based hash functions, 
                         resulting in the state-of-the-art extended PLSH approach (ePLSH). 
                         The proposed approach is evaluated in the dataset FERET and in the 
                         dataset FRGCv1. The results show a significant reduction in the 
                         number of subjects evaluated in the face identification (reduced 
                         to 0.3% of the gallery), providing averaged speedups up to 233 
                         times compared to evaluating all subjects in the face gallery and 
                         58 times compared to previous works in the literature.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M9MN42",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M9MN42",
           targetfile = "main.pdf",
        urlaccessdate = "2024, May 03"
}