文件列表:

指纹识别的matlab源码\gabor2d_sub.m (642, 2004-04-26)
指纹识别的matlab源码\19_7.bmp (66614, 2001-01-11)
指纹识别的matlab源码\37_3.bmp (66614, 2001-01-11)
指纹识别的matlab源码\37_5_2.bmp (66614, 2001-01-11)
指纹识别的matlab源码\37_7.bmp (66614, 2001-01-11)
指纹识别的matlab源码\22443.bmp (132118, 2000-08-21)
指纹识别的matlab源码\sector_norm.m (2773, 2004-04-26)
指纹识别的matlab源码\whichsector.m (1203, 2004-04-26)
指纹识别的matlab源码\Cropping.m (1947, 2004-05-22)
指纹识别的matlab源码\vedicentro.m (499, 2004-06-19)
指纹识别的matlab源码\mirror.m (424, 2004-06-19)
指纹识别的matlab源码\recrop.m (122, 2004-06-19)
指纹识别的matlab源码\conv2fft.m (6943, 2004-06-19)
指纹识别的matlab源码\centralizing.m (11549, 2004-06-23)
指纹识别的matlab源码\fprec.m (20312, 2004-06-28)
指纹识别的matlab源码\old_version.zip (298672, 2004-06-29)
指纹识别的matlab源码\Thumbs.db (30208, 2008-05-15)
指纹识别的matlab源码 (0, 2005-04-23)

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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