@InProceedings{MasiWuHassNata:2018:DeFaRe,
               author = "Masi, Iacopo and Wu, Yue and Hassner, Tal and Natarajan, Prem",
          affiliation = "Information Sciences Institute (ISI), University of Southern 
                         California (USC) and Information Sciences Institute (ISI), 
                         University of Southern California (USC) and The Open University of 
                         Israel, Raanana, Israel and Information Sciences Institute (ISI), 
                         University of Southern California (USC)",
                title = "Deep Face Recognition: a Survey",
            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 = "face recognition, deep learning, survey.",
             abstract = "Face recognition made tremendous leaps in the last five years with 
                         a myriad of systems proposing novel techniques substantially 
                         backed by deep convolutional neural networks (DCNN). Although face 
                         recognition performance sky-rocketed using deep-learning in 
                         classic datasets like LFW, leading to the belief that this 
                         technique reached human performance, it still remains an open 
                         problem in unconstrained environments as demonstrated by the newly 
                         released IJB datasets. This survey aims to summarize the main 
                         advances in deep face recognition and, more in general, in 
                         learning face representations for verification and identification. 
                         The survey provides a clear, structured presentation of the 
                         principal, state-of-the-art (SOTA) face recognition techniques 
                         appearing within the past five years in top computer vision 
                         venues. The survey is broken down into multiple parts that follow 
                         a standard face recognition pipeline: (a) how SOTA systems are 
                         trained and which public data sets have they used; (b) face 
                         preprocessing part (detection, alignment, etc.); (c) architecture 
                         and loss functions used for transfer learning (d) face recognition 
                         for verification and identification. The survey concludes with an 
                         overview of the SOTA results at a glance along with some open 
                         issues currently overlooked by the community.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "29 Oct.-1 Nov. 2018",
                  doi = "10.1109/SIBGRAPI.2018.00067",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00067",
             language = "en",
                  ibi = "8JMKD3MGPAW/3RQEGQE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3RQEGQE",
           targetfile = "PID5564503.pdf",
        urlaccessdate = "2024, May 06"
}