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@InProceedings{PintoPedrSchwRoch:2012:ViFaSp,
               author = "Pinto, Allan da Silva and Pedrini, Helio and Schwartz, William 
                         Robson and Rocha, Anderson",
          affiliation = "{University of Campinas} and {University of Campinas} and 
                         {Universidade Federal de Minas Gerais} and {University of 
                         Campinas}",
                title = "Video-Based Face Spoofing Detection through Visual Rhythm 
                         Analysis",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Biometrics, Video-based Face Spoofing, Visual Rhythm, Gray-Level 
                         Co-occurrence Matrix.",
             abstract = "Recent advances on biometrics, information forensics, and security 
                         have improved the accuracy of biometric systems, mainly those 
                         based on facial information. However, an ever-growing challenge is 
                         the vulnerability of such systems to impostor attacks, in which 
                         users without access privileges try to authenticate themselves as 
                         valid users. In this work, we present a solution to video-based 
                         face spoofing to biometric systems. Such type of attack is 
                         characterized by presenting a video of a real user to the 
                         biometric system. To the best of our knowledge, this is the first 
                         attempt of dealing with video-based face spoofing based in the 
                         analysis of global information that is invariant to video content. 
                         Our approach takes advantage of noise signatures generated by the 
                         recaptured video to distinguish between fake and valid access. To 
                         capture the noise and obtain a compact representation, we use the 
                         Fourier spectrum followed by the computation of the visual rhythm 
                         and extraction of the gray-level co-occurrence matrices, used as 
                         feature descriptors. Results show the effectiveness of the 
                         proposed approach to distinguish between valid and fake users for 
                         video-based spoofing with near-perfect classification results.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "paper_camera_ready.pdf",
        urlaccessdate = "2021, Jan. 28"
}


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