Fingerprint matching using new type of minutae features
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.
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.
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
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
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