Fingerprint matching using new type of minutae features

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Project Owner : jeeva4life
Created Date : Sun, 08/04/2012 - 23:57
Project Description :



Fingerprint matching is challenging, as the matcher has to minimize two competing error rates: the False Accept Rate and the False Reject Rate. We propose a novel, efficient, accurate and distortion-tolerant fingerprint authentication technique based on graph representation. Using the fingerprint minutiae features, a labeled and weighted graph of minutiae is constructed for both the query fingerprint and the reference fingerprint. In the first phase, we obtain a minimum set of matched node pairs by matching their neighborhood structures. In the second phase, we include more pairs in the match by comparing distances with respect to matched pairs obtained in first phase. An optional third phase, extending the neighborhood around each feature, is entered if we cannot arrive at a decision based on the analysis in first two phases. The proposed algorithm has been tested with excellent results on a large private live scan database obtained with optical scanners.

               A fingerprint is the impression made by the papillary ridges on the ends of the fingers and thumbs. Fingerprints afford an infallible means of personal identification, because the ridge arrangement on every finger of every human being is unique and does not alter with growth or age. Fingerprints serve to reveal an individual's true identity despite personal denial, assumed names, or changes in personal appearance resulting from age, disease, plastic surgery, or accident. The practice of utilizing fingerprints as a means of identification, referred to as dactyloscopy, is an indispensable aid to modern law enforcement.



Existing System:


ØThe Existing System only used an image based approach.


ØThis system does not support the minutia approach


ØThis system also takes long time identification.


ØThis system result should not accurate.


Proposed System:



ØIn our method the   ridge features and conventional minutiae features (minutiae type, orientation, and position).


ØRidge  features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type.


ØBreadth-first search (BFS) is performed to detect the matched ridge-based

coordinate pairs.







Software Requirements

Platform                                   : JDK 1.5

Program Language          : JAVA

Tool                                        : NETBEANS 5.5

Data Base                                : MS Access          , My Sql

Operating System                    : Microsoft Windows NT 4.0 or  Windows 2000or XP




Hardware Requirements

Processor                                : 733 MHz Pentium III Processor

RAM                                       : 128 MB

Hard Drive                               : 10GB

Monitor                                    : 14” VGA COLOR MONITOR                           

Keyboard                                      : 104 Keys  

Floppy Drive                            : 1.44 MB

Mouse                                     : Logitech Serial Mouse

Disk Space                               : 1 GB

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