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Minutia matching is the most popular approach to
fingerprint recognition. In this paper, we analyzed a novel
fingerprint feature named adjacent orientation vector, or
AOV, for fingerprint matching. In the first stage, AOV is
used to find possible minutiae pairs. Then one minutiae
set is rotated and translated. This is followed by a
preliminary matching to ensure reliability as well as a
fine matching to overcome possible distortion. Such
method has been deployed to a payroll and security
access information system and its workability is
encouraging. The information system aims to offer a
highly secured and automated identification system for
payroll tracking as well as authorized access to working
areas.
Because of uniqueness, as a personal identification
method, fingerprint has been widely used in the past
decades. The most popular matching strategy for
fingerprint identification is minutiae matching. The
simplest pattern of the minutiae-based representations
consists of a set of minutiae, including ridge endings and
bifurcations defined by their spatial coordinates. Each
minutia is described by its spatial location associated with
the orientation. Although a set of minutiae has been
widely used for matching, the noise problem in a
fingerprint image has not been solved. The disadvantage
of minutiae based method is the lack of robustness, there
are some alternative methods proposed, for instance,
Jain’s filterbank method and Isenor and Zaky’s graph
matching method.
The feature vector of minutia generally consists of the
minutia type, the coordinates and the tangential angle of
the minutia. The automatic fingerprint
verification/identification is then achieved with a kind of
point pattern matching instead of the fingerprint image
matching. Several point pattern matching algorithms have
been proposed in the literature. The point pattern
matching is generally intractable because the
correspondences between the two point sets of template
and input fingerprint are unknown. The minutia
correspondences are difficult to obtain due to several
factors such as the rotation, translation and deformation of
the fingerprints, the location and direction errors of the
detected minutiae as well as the presence of spurious
minutiae and the absence of genuine minutiae.
This package uses Peter Kovesi's code
for fingerprint enhancement, "MATLAB and Octave Functions for Computer Vision and Image Processing" and it is based on
the paper "Adjacent orientation vector based fingerprint minutiae matching system", G. S. Ng, X. Tong, X. Tang and D. Shi,
Pattern Recognition, ICPR 2004. This article is available at http://citeseer.ist.psu.edu/739574.html.
We have tested the code with Set "A" of FVC2004 Database, using 100 classes,
N fingerprint images randomly selected for training (totally N*100 images)
and 8-N fingerprint images used for testing (totally 800-N*100 images), without any overlapping between training and testing images,
obtaining the following results (R is the recognition rate):
- N = 4, R = 88.40%
- N = 2, R = 79.48%
- N = 1, R = 60.59%
Index Terms: Matlab, source, code, adjacent, orientation, vector, fingerprint, minutiae, matching, system, recognition, verification, identification.
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Figure 1. Fingerprint sensor |
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A simple and effective source code for Fingerprint Identification. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox is required. |
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Release |
Date |
Major features |
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1.0 |
2008.02.26 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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AOV based Fingerprint Minutiae Matching System - Release 1.0 - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 150 EUROS (less
than 210 U.S. Dollars). |
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of AOV based Fingerprint Minutiae Matching System. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 14 SP1. Matlab Image Processing Toolbox is required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
