fingerprint
所属分类:matlab编程
开发工具:matlab
文件大小:553KB
下载次数:132
上传日期:2015-04-14 15:29:07
上 传 者:
Alicedada
说明: 基于matlab的指纹识别,自动识别指纹库中的指纹,使用简单正确率高。
(Matlab based on fingerprint recognition, automated fingerprint identification fingerprint , simple to use correct rate.)
文件列表:
fingernew (0, 2015-04-02)
fingernew\1.bmp (66614, 2001-01-11)
fingernew\10.bmp (125718, 2015-01-10)
fingernew\11.bmp (115830, 2015-01-10)
fingernew\12.bmp (115126, 2015-01-10)
fingernew\13.bmp (128342, 2015-01-10)
fingernew\14.bmp (121198, 2015-01-10)
fingernew\15.bmp (118202, 2015-01-10)
fingernew\16.bmp (111794, 2015-01-10)
fingernew\17.bmp (115738, 2015-01-10)
fingernew\18.bmp (113554, 2015-01-10)
fingernew\19.bmp (112862, 2015-01-10)
fingernew\2.bmp (66614, 2001-01-11)
fingernew\3.bmp (66614, 2001-01-11)
fingernew\4.bmp (66614, 2001-01-11)
fingernew\5.bmp (132118, 2000-08-21)
fingernew\6.bmp (114790, 2015-01-10)
fingernew\7.bmp (105054, 2015-01-10)
fingernew\8.bmp (127790, 2015-01-10)
fingernew\9.bmp (125878, 2015-01-10)
fingernew\Thumbs.db (48128, 2005-04-20)
fingernew\centralizing.m (11549, 2004-06-23)
fingernew\conv2fft.m (6943, 2004-06-19)
fingernew\cropping.m (1945, 2015-01-10)
fingernew\gabor2d_sub.m (642, 2004-04-26)
fingernew\main.m (9569, 2015-01-10)
fingernew\mirror.m (424, 2004-06-19)
fingernew\recrop.m (122, 2004-06-19)
fingernew\sector_norm.m (2773, 2004-04-26)
fingernew\seventeen.bmp (115738, 2015-01-10)
fingernew\six.bmp (114790, 2015-01-10)
fingernew\three.bmp (66614, 2001-01-11)
fingernew\vedicentro.m (499, 2004-06-19)
fingernew\whichsector.m (1203, 2004-04-26)
Unzip all files into Matlab current directory and type
"fprec" to start fingerprint image processing.
Type "helpwin fprec" for more detailed informations.
Notes:
old_version.zip is the previous release (Fingerprint Recognition System 2.0)
% 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.
%
% Type "fprec" on Matlab command window to start image processing.
% This source code provides a new, improved GUI respect to the previous release.
% Simulation parameters can be changed in this file:
%
% n_bands: the number of concentric bands
% h_bands: the width of each band in pixels
% n_arcs: the number of arcs (each band has exactly n_arcs arcs)
% h_radius: the inner radius in pixel (the central band is not considered
% because it is a too small area)
% num_disk: the number of gabor filters
%
% n_sectors and h_lato rapresents respectively the total number of sectors
% (the length of the feature vector associated to each filter of filter-bank:
% there are num_disk gabor filters) and the height of the cropped image in pixels.
% N_secors and h_lato should not be changed.
%
%
% $Revision: 1.0 $ $Date: 2002.10.02 $
% $Revision: 2.0 $ $Date: 2003.11.29 $
% $Revision: 3.0 $ $Date: 2004.06.22 $ by Luigi Rosa
% email: luigi.rosa@tiscali.it
% mobile: +393403463208
% website: http://utenti.lycos.it/matlab
%
%
% Modified respect to the previous version:
%
% - Major bugs fixed
% - New GUI
% - 8 Gabor filters 0 22.5 45 67.5 90 112.5 135 157.5 degree
% - Convolution is performed in frequency domain
% - DataBase
% - Fingerprint matching
% - Error management
% - Complex filtering techniques
% - Improved core point determination
% - Robustness against noise
% - Modifiable simulation parameters
%
% Input fingerprint should be 256 x 256 image 8-bit grayscale @ 500 dpi.
% If these conditions are not verified some parameters in m-functions
% should be changed in a proper way (such as, for example, Gabor filter
% parameters in gabor2d_sub function). See the cited references for more
% details.
%
% M-files included:
%
% -fprec.m: this file. It initializes the entire image processing. The simulation
% parameters can be changed in this main file.
% -centralizing.m: a function which accept an input image and determines the coordinates
% of the core point. The core point is determinated by complex filtering.
% The region of interest is determinated fixing a minimum threshold value
% for the variance. Input image is divided into non-overlapping blocks and
% only blocks with a variance smaller than this threshold value are considered
% background. The logical matrix (associated to the region of interest) is first
% closed (Matlab function imclose), then eroded (Matlab function imerode) with two
% given structuring elements. The image is "mirrored" before convolution with complex
% filter, then it is re-cropped to its original sizes.
% -mirror.m: a function which is used to "mirror" input image in order to avoid undesired
% boundary effects (function used by centralizing.m).
% -recrop.m: a function used to resize the mirrored filtered image (function used by centralizing.m)
% -conv2fft.m: this function performs 2D FFT-based convolution. Type "help conv2fft" on Matlab command
% window for more details.
% -whichsector.m: a function used to determine (for each pixel of the cropped image) the corresponding
% sectors of the concentric bands (function used by sector_norm.m).
% -sector_norm.m: a function used to normalize input image and to calculate the features vector
% -cropping.m: this function is used to cropp the input fingerprint image after the core point is
% determinated.
% -gabor2d_sub.m: a function used to calculate the coefficients of the gabor 2D filters.
% -vedicentro.m: this simple routines uses the M-function centralizing.m and it is used to display
% the core point.
%
%
% A crucial step in fingerprint recognition is core point determination.
% If any error occurs while cropping image you can use the auxiliary m-file
% "vedicentro.m": it visualizes the input fingerprint and the core point
% calculated by the m-function "centralizing.m".
%
%
% Notes:
% The computational load can be significantly reduced by recursive filtering techniques.
% For a complete publication list of Lucas J. van Vliet please visit the following URL:
% http://www.ph.tn.tudelft.nl/~lucas/publications/papersLJvV.html
% Here you will find articles concernings a recursive implementation of the Gaussian filter,
% of the derivative Gaussian filter and of Gabor filter.
%
% If you want to optimize the proposed method an excellent article is the following one:
% Erian Bezhani, Dequn Sun, Jean-Luc Nagel, and Sergio Carrato, "Optimized filterbank
% fingerprint recognition", Proc. SPIE Intern. Symp. Electronic Imaging 2003, 20-24
% Jan. 2003, Santa Clara, California.
%
% This code was developed using:
% MATLAB Version 6.5 and Image Processing Toolbox Version 3.2 (R13)
% Operating System: Microsoft Windows 2000 Version 5.0 (Build 2195: Service Pack 4)
% Java VM Version: Java 1.3.1_01 with Sun Microsystems Inc. Java HotSpot(TM) Client VM
%
% Please contribute if you find this software useful.
% Report bugs to luigi.rosa@tiscali.it
%
%
% References:
%
% Cheng Long Adam Wang, researcher
% Fingerprint Recognition System
% http://home.kimo.com.tw/carouse9/FRS.htm
%
% 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
%
% Final Report 18-551 (Spring 1999) Fingerprint Recognition Group Number 19
% Markus Adhiwiyogo, Samuel Chong, Joseph Huang, Weechoon Teo
% http://www.ece.cmu.edu/~ee551/Old_projects/projects/s99_19/finalreport.html
%
% Kenneth Nilsson and Josef Bigun, "Localization of corresponding points in
% fingerprints by complex filtering", Pattern Recognition Letters, 24 (2003) 2135-2144
% School of Information Science, Computer and Electrical Engineering (IDE), Halmstad
% University, P.O. Box 823, SE-301 18, Halmstad, Sweden.
%
%
% ************************************************************************
% This code required a lot of time to be developed. Please send the author
% money, food, drinks or improvements to the code itself.
% This is my postal address:
% 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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