Maltoni, "Inexact Graph Matching for Fingerprint Classification", Machine GRAPHICS & VISION Special Issue on Graph Trasformations in Pattern Generation and CAD, vol.8, no.2, pp.231-248, September 1999. Maltoni, "A Multi-Classifier Approach to Fingerprint Classification", Pattern Analysis and Applications Special Issue on Fusion of Multiple Classifiers, vol.5, no.2, pp.136-144, May 2002. Nanni, "FuzzyBagging: a novel ensemble of classifiers", Pattern Recognition, vol.39, no.3, pp.488-490, March 2006. Maltoni, "On the Spatial Distribution of Fingerprint Singularities", IEEE Transactions on Pattern Analysis Machine Intelligence, vol.31, no.4, pp.742-748, April 2009. Maltoni, "Fingerprint Indexing based on Minutia Cylinder-Code", IEEE Transactions on Pattern Analysis Machine Intelligence, vol.33, no.5, pp.1051-1057, May 2011. Maio, "Candidate List Reduction based on the Analysis of Fingerprint Indexing Scores", IEEE Transactions on Information Forensics and Security, vol.6, no.3, pp.1160-1164, September 2011. Cappelli, "Fast and Accurate Fingerprint Indexing based on Ridge Orientation and Frequency", IEEE Transactions on Systems, Man and Cybernetics - Part B, vol.41, no.6, pp.1511-1521, December 2011. Ferrara, "A Fingerprint Retrieval System Based on Level-1 and Level-2 Features", Expert Systems With Applications, vol.39, no.12, pp.10465-10478, September 2012. Bolle, Automatic Fingerprint Recognition Systems, Springer, 2004. Maio, "The State of the Art in Fingerprint Classification", in N. Prabhakar, Handbook of Fingerprint Recognition (Second Edition), Springer (London), 2009. Feng, Handbook of Fingerprint Recognition (Third Edition), Springer Nature, 2022.ĭ. In reality, fingerprints are not uniformly distributed among these five classes: the proportions have been estimated as 3.7%, 2.9%, 33.8%, 31.7% and 27.9% for Arch, Tented arch, Left loop, Right loop and Whorl, respectively.īibliography (Click here if you are interested in any of the publications below)ĭ. Five classes (Arch, Tented arch, Left loop, Right loop and Whorl) are commonly used by today’s fingerprint classification techniques. While fingerprint matching is usually performed according to fingerprint micro-features, such as ridge terminations and bifurcations (minutiae), fingerprint classification is usually based on macro-features, such as global ridge structure.Īll the classification schemes currently used by police agencies are variants of the so-called Henry’s classification scheme.
A common strategy to reduce the number of comparisons during fingerprint retrieval and, consequently, to improve the response time of the identification process, is to divide the fingerprints into some predefined classes.įingerprint classification means assigning each fingerprint to a class in a consistent and reliable way, such that an unknown fingerprint to be searched, needs to be compared only with the subset of fingerprints in the database belonging to the same class. The identification of a person requires the comparison of his/her fingerprint with all the fingerprints in a database, which in large scale applications may be very large (several million fingerprints).
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