Fingerprints are the oldest and most widely used form of biometric identification. Despite the widespread use of fingerprints, there is little statistical theory on the Uniqueness of fingerprint minutiae. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded and corrupted due to variations in skin and impression conditions. Thus, image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations.
In this dissertation, I firstly provide discussion on the methodology and implementation of techniques for fingerprint image enhancement and minutiae extraction. Experiments using a mixture of both synthetic test images and real fingerprint images are then conducted to evaluate the performance of the implemented techniques. In combination with these techniques, preliminary results on the statistics of fingerprint images are then presented and discussed.
Aim of the Project
To match a Fingerprint image with a one already stored in the database.
A fingerprint image essentially consists of a set of minutiae on the plane.
Minutiae are the terminations and bifurcations of ridge lines in a fingerprint image.
A new approach towards fingerprint recognition is to match the distribution and orientation of such points.
Motivation behind it
Finger-print recognition is used in various systems for Verification, Identification etc.
Recognizing manually can be very time consuming and costly.
There are systems already in use which use similar technology and a lot of research is going on to improve the technique.
Algorithm
This particular method of fingerprint matching consists mainly of six stages ….
(i) Image Enhancement,
(ii) Ridge extraction
(iii) Binarization
(iv) Thinning
(v) Minutiae extraction
(vi) Post processing.
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