Minutiae Based Geometric Hashing for Fingerprint Database

Umarani, J.; Viswanathan, J.; Gupta, Aman Kishore; Gupta, Phalguni

doi:10.1007/978-3-642-31837-5_61

Cited by 3 publications

(1 citation statement)

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“…However we will use the triplets contained in a minutiae vicinity, which is defined in [15], instead of applying Delaunay triangulation, since fingerprint sample noise and other distortions can seriously affected the whole structure by using Delaunay triangulation [16]. We extract nine features from a triplet which is contained in a minutiae vicinity and use these features to design an indexing scheme, which we call Minutiae-VicinityIndex (MV-Index) method.…”

Section: Introductionmentioning

confidence: 99%

A score-level fusion fingerprint indexing approach based on minutiae vicinity and minutia cylinder-code

Li¹,

Yang²,

Busch³

2014

2nd International Workshop on Biometrics and Forensics

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Due to the uniqueness and permanence properties of the biometric fingerprint characteristic, large scale in border control and governmental applications such as the Visa Information System (VIS) in Europe, US-VISIT / IDENT system in the USA and the Aadhaar project in India are based on fingerprint recognition. These systems generally contain millions of fingerprint samples. In order to improve the efficiency in seeking for suitable candidate reference data in such large-scale databases, studying indexing techniques for fingerprints is desirable. In this paper, we design a new indexing method using the features extracted from minutia details including location, direction and ridge information. Then a score-level fusion indexing approach is proposed by combing this new method with the minutia cylinder-code (MCC) indexing method. The results demonstrate the improvement of the proposed approach based on experiments on several public fingerprint databases.

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Section: Introductionmentioning

confidence: 99%