Abstract
Nowadays, palmprint identification has emerged as a very competitive and used technique in biometric systems. In these applications, efficiency is a very important but challenging problem for some reasons. The area of a palmprint is bigger than the one in a fingerprint. In this way, the amount of minutiae is also bigger and distortions are much more critical. On the other hand, reduction of the search space is essential in the process of identification. In this paper, a new palmprint indexing algorithm based on minutiae is proposed. Minutiae are very used by experts in order to perform manual matching, so this proposal can use features corrected by humans. The presented algorithm also uses a representation of palmprints based on an expanded triangle set, that proves to be very tolerant to minutia displacements on impressions. With this representation, a small set of features is extracted from minutia triplets. This aspect is very critical in the context of palmprints where the amount of minutiae can be over 900. The accuracy reached by this method in the performed experiments, is higher than \(99,5\,\%\) for any value of penetration rate.
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Muñoz-Briseño, A., Hernández-Palancar, J., Gago-Alonso, A. (2015). Minutiae Based Palmprint Indexing. In: Garain, U., Shafait, F. (eds) Computational Forensics. IWCF IWCF 2012 2014. Lecture Notes in Computer Science(), vol 8915. Springer, Cham. https://doi.org/10.1007/978-3-319-20125-2_2
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DOI: https://doi.org/10.1007/978-3-319-20125-2_2
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