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Fingerprint classification techniques

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Klimanee, C (2004) Fingerprint classification techniques. Research Master thesis, University of Tasmania.

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Abstract

Today, most biometrics research groups are tackling the challenging problem of an
automatic fingerprint identification system (AFIS) using large databases. Since AFIS
dedicates most of its processing time to searching for the best-matched fingerprint,
searching over the entire fingerprint database is rather inefficient. It is proposed that
the database be divided into sub-databases, each containing only fingerprints of the
same pattern or class. Fingerprint classification is then an important first step in
directing the search only to the appropriate sub-database, thus reducing the extent of
searching of the large database.
The main objective of this thesis is to propose a classification technique to reliably
classify a fingerprint into one of six well-known classes: plain arch, tented arch, right
loop, left loop, whorl and twin loop. The fingerprint classification technique proposed
in this thesis has achieved good results owing to the improvement in a number of
processing steps the author has proposed for the enhancement of fingerprints, the
determination of singular points and their associated principal axes, and the rule-based
classification algorithm. The directional bandpass Gabor filter-bank approach is one of the most effective and
mathematically elegant techniques to date for fingerprint image enhancement. The
filter output, however, is very sensitive to the ridge orientation and frequency that the
filter is tuned to, and also to the spatial parameters of the Gaussian envelope.
Unfortunately, filtering of a fingerprint image with an adaptive two-dimensional
Gabor filter bank is computationally expensive because ridge orientation and
frequency vary significantly throughout the fingerprint. In this thesis we propose to
use an array of 8x4 two-dimensional Gabor filters tuned to eight directions and four
ridge frequencies. Filtered fingerprint images at any combination of local ridge
orientation and frequency can be calculated using a 2-D interpolation algorithm. The
proposed technique produces a better quality of image than current Gabor-based
techniques. The results are compared using a goodness index measure of the
reliability of the automatic minutiae detection. The accuracy of the location of singular points on a fingerprint is important for
minutiae matching alignment and is also essential for the Poincare index to correctly
determine the type of singular points. In this thesis, we present a novel yet simple and
accurate technique for the automatic determination of singular points. The technique
offers a double-resolution estimation of the ridge orientation on a 4x4 pixels quincunx
grid and quantises ridge orientations into six codes called ridge flow codes. Singular
regions are defined as where all six codes exist. A singular point within a singular
region can then be quickly determined by locating the point where the variance of
local ridge orientation is at its maximum. The Poincare indices of these singular
points are used to determine their type: ordinary, delta, core or double-core. The
distribution and type of the singular points, together with their associated principal
axes, are then used to classify a fingerprint into one of six well-known classes or
patterns.
This thesis proposes a rule-based algorithm for classifying fingerprints into one of six
well-known classes. The rules are formed using the relative locations and types of
singular points and the relative directions of their associated principal axes. The
reliable and fast classification algorithm is made possible by a simple but effective
combination of ridge flow-code technique and orientation variance calculation in the
determination of singular points and principal axes. The Poincare indices of these
singular points are used to determine their type: ordinary, delta, core or double-core.
For a test sample of some 150 fingerprints, the correct classification rate of the
proposed algorithm was found to be better than 90%.

Item Type: Thesis (Research Master)
Keywords: Fingerprints
Copyright Holders: The Author
Copyright Information:

Copyright 2004 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

Thesis (M.Eng.Sc.)--University of Tasmania, 2004. Includes bibliographical references

Date Deposited: 19 Dec 2014 02:47
Last Modified: 05 Jul 2017 01:44
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