Fingerprint Indexing Based on Minutia Cylinder-Code

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 33; no. 5; pp. 1051 - 1057
• Main Authors: Cappelli, Raffaele, Ferrara, Matteo, Maltoni, Davide
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.05.2011; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation; Artificial intelligence; Biometric Identification - methods; Computer science; control theory; systems; Cylinders; Databases, Factual; Derivation; Dermatoglyphics; Exact sciences and technology; Fingerprint recognition; Fingerprints; Fingers; Hamming distance; Humans; identification; Image Processing, Computer-Assisted; Indexing; Information systems. Data bases; locality-sensitive hashing; Mathematical analysis; Mathematical models; Memory organisation. Data processing; minutiae cylinder-code; NIST; Pattern recognition. Digital image processing. Computational geometry; Similarity; Software; Vectors (mathematics); Numerical approximation; locality-sensitive hashing; Invariant; identification; Fingerprints; Locality; Fingerprint identification; Hashing; minutiae cylinder-code; Search algorithm; Indexing
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2010.228

This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.