| ginitree |
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- Classification:Numerical Algorithm-Artificial Intelligence - AI-NN-PR
- Development Tool:C-C++
- Sise:315 KB
- Upload time:2012/7/18 7:22:24
- Uploader:xavito
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| Description |
giniTree.cpp and giniTree.hpp will train a decision tree using the gini index to determine split attributes and split points
File list:
ginitree
.......\giniNtest.cpp
.......\giniNtest.exe
.......\giniTree.cpp
.......\giniTree.exe
.......\giniTree.h
.......\giniTree.hpp
.......\gpl.txt
.......\iris_test.dat
.......\makefile
.......\Readme.txt
.......\tst_iris.dat
.......\tst_iris.dat.out
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| Related source code |
[finger recoginition] - Unzip all files into Matlab current directory and type
"fprec" to start fingerprint image processing.
Filterbank-Based Fingerprint Matching (A.K.Jain, S.Prabhakar, L.Hong and S.Pankanti, 2000)
Abstract
With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on
the emerging automatic personal identification applications, biometrics-based verification, especially
fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings
of the traditional approaches to fingerprint representation. For a considerable fraction of population,
the representations based on explicit detection of complete ridge structures in the fingerprint are
difficult to extract automatically. The widely used minutiae-based representation does not utilize a
significant component of the rich discriminatory information available in the fingerprints. Local ridge
structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty
in quickly matching two fingerprint images containing different number of unregistered minutiae points.
The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details
in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean
distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a
verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms
published in the open literature. Our system performs better than a state-of-the-art minutiae-based system
when the performance requirement of the application system does not demand a very low false acceptance rate.
Finally, we show that the matching performance can be improved by combining the decisions of the matchers
based on complementary (minutiae-based and filter-based) fingerprint information.
Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification.
This code was developed using:
MATLAB Version 7.0.1 and Image Processing Toolbox Version 5.0.1 (R14SP1)
Operating System: Microsoft Windows 2000 Version 5.0 (Build 2195: Service Pack 4)
Java VM Version: Java 1.4.2_04 with Sun Microsystems Inc. Java HotSpot(TM) Client VM
References:
A. K. Jain, S. Prabhakar, and S. Pankanti, "A Filterbank-based Representation for
Classification and Matching of Fingerprints", International Joint Conference on
Neural Networks (IJCNN), pp. 3284-3285, Washington DC, July 10-16, 1999.
http://www.cse.msu.edu/~prabhaka/publications.html
"Fingerprint Classification and Matching Using a Filterbank", Salil Prabhakar
A DISSERTATION Submitted to Michigan State University in partial fulfillment
of the requirements for the degree of DOCTOR OF PHILOSOPHY, Computer
Science & Engineering, 2001
http://biometrics.cse.msu.edu/SalilThesis.pdf
************************************************************************
Luigi Rosa
Via Centrale 35
67042 Civita di Bagno
L'Aquila --- ITALY
mobile +39 340 3463208
email luigi.rosa@tiscali.it
website http://utenti.lycos.it/matlab
*************************************************************************
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