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@InProceedings{PaulinoJainFeng:2010:FuMaMa,
               author = "Paulino, Alessandra Aparecida and Jain, Anil K. and Feng, 
                         Jianjiang",
          affiliation = "{Michigan State University} and {Michigan State University} and 
                         {Tsinghua University}",
                title = "Latent fingerprint matching: fusion of manually marked and derived 
                         minutiae",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
         organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "latent fingerprint, fingerprint matching, rolled fingerprint, 
                         enhancement, minutiae extraction, interoperability.",
             abstract = "Matching unknown latent fingerprints lifted from crime scenes to 
                         full (rolled or plain) fingerprints in law enforcement databases 
                         is of critical importance for combating crime and fighting 
                         terrorism. Compared to good quality full fingerprints acquired 
                         using live-scan or inking methods during enrollment, latent 
                         fingerprints are often smudgy and blurred, capture only a small 
                         finger area, and have large nonlinear distortion. For this reason, 
                         features (minutiae and singular points) in latents are typically 
                         manually marked by trained latent examiners. However, this 
                         introduces an undesired interoperability problem between latent 
                         examiners and automatic fingerprint identification systems (AFIS); 
                         the features marked by examiners are not always compatible with 
                         those automatically extracted by AFIS, resulting in reduced 
                         matching accuracy. While the use of automatically extracted 
                         minutiae from latents can avoid interoperability problem, such 
                         minutiae tend to be very unreliable, because of the poor quality 
                         of latents. In this paper, we improve latent to full fingerprint 
                         matching accuracy by combining manually marked (ground truth) 
                         minutiae with automatically extracted minutiae. Experimental 
                         results on a public domain database, NIST SD27, demonstrate the 
                         effectiveness of the proposed algorithm.",
  conference-location = "Gramado",
      conference-year = "Aug. 30 - Sep. 3, 2010",
             language = "en",
           targetfile = "PID1395615.pdf",
        urlaccessdate = "2020, Nov. 25"
}


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