@InProceedings{WangRamoFierKris:2013:ToReFu,
               author = "Wang, Ruifang and Ramos, Daniel and Fierrez, Julian and Krish, Ram 
                         P.",
          affiliation = "{Universidad Autonoma de Madrid} and {Universidad Autonoma de 
                         Madrid} and {Universidad Autonoma de Madrid} and {Universidad 
                         Autonoma de Madrid}",
                title = "Towards Regional Fusion for High-Resolution Palmprint 
                         Recognition",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "High resolution palmprints, regional fusion.",
             abstract = "The existing high resolution palmprint matching algorithms 
                         essentially follow the minutiae-based fingerprint matching 
                         strategy and focus on full-to-full/partial-to-full palmprint 
                         comparison. These algorithms would face problems when they are 
                         applied to forensic palmprint recognition where latent marks have 
                         much smaller area than full palmprints. Therefore, towards 
                         forensic scenarios, we propose a novel matching strategy based on 
                         regional fusion for high resolution palmprint recognition using 
                         regions segmented by major creases features. The matching strategy 
                         includes two stages: 1) region-to-region palmprint comparison; 2) 
                         regional fusion at score level. We first studied regional 
                         discriminability of a high resolution palmprint under the concept 
                         of three regions, i.e., interdigital, hypothenar and thenar, which 
                         is the most significant difference between palmprits and 
                         fingerprints. Then we implemented regional fusion based on 
                         logistic regression at score level using region-to-region 
                         comparison scores obtained by a commercial SDK, MegaMatcher 4.0. 
                         Significant improvement of recognition accuracy is achieved by 
                         regional fusion on a public high resolution palmprint database 
                         THUPALMLAB. The EER of logistic regression based regional fusion 
                         is 0.25%, while the EER of full-to-full palmprint comparison is 
                         1%.",
  conference-location = "Arequipa, Peru",
      conference-year = "5-8 Aug. 2013",
                  doi = "10.1109/SIBGRAPI.2013.56",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2013.56",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3EEQDE8",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3EEQDE8",
           targetfile = "Camera_Ready_Towards Regional Fusion for High Resolution Palmprint 
                         Recognition.pdf",
        urlaccessdate = "2024, May 02"
}