Digital Image Processing - (monsoon 2003) FINAL PROJECT REPORT Project Members Sanyam Sharma - 200101072 Sunil Mohan Ranta - 200101083 Group No. - 15 FINGERPRINT.

Digital Image Processing - (monsoon 2003) FINAL PROJECT REPORT

Project MembersSanyam Sharma - 200101072Sunil Mohan Ranta - 200101083

Group No. - 15

FINGERPRINT MATCHING

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.

Ridge Detection

As alluded earlier, the objective of the ridge detection algorithm is to separate ridges from the valleys in a given fingerprint image.

A more reliable property of the ridges in a fingerprint image is that the gray level values on ridges attain their local minima along a direction normal to the local ridge orientation.

Image Enhancement and Binarization

Removing noise and sharpening the ridges using various filters. eg. Gabor Filter

Making a binary image from the enhanced image.

Ridges in black color on a white background.

Thinning

The objectives of this step is to obtain a thinned image using morphological filters on binary images.

All the ridges are only 1- pixel thick.

Minutiae DetectionOnce the thinned ridge map is available, the ridge pixels with three ridge pixel neighbors are identified as Ridge bifurcations and those with one ridge pixel neighbor are identified as Ridge endings.

Building a minutiae skeleton

Set of distances between ridge bifurcating and ridge ending minutiaes.

Distribution of minutiaes.

Orientation of minutiaes.

Matching the details …

Comparing the obtained skeleton and minutiae score with the other image.

There can be many ways to match the details obtained.

One approach can be using a skeleton structure of minutiae points.

Overall Process

Image Enhancement and Ridge Detection

Binarization ThinningSensor

Matching

Result

Fingerprint Database Minutiae Extraction

Applications …

Fingerprint Matching.Identifiers.

Fingerprint Verification.Secure access, digital signatures etc.

Results

After Enhancement

We have achieved Appreciable enhancement using Gabor filters.

Features handled

- ridge enhancement Binarization of image using threshold values.

Thinning

Reducing width of ridges to a ‘single’ pixel. Algorithm used

Morphological thinning.

Minutiae Detection

Next step is to detect Minutiae in the image. We have achieved quite efficient resultsin detecting

all the minutiae points. Removal of False minutiae points.

Matching of minutiae sets

Algorithms Used Relative Distance Matching Using Quad Tree Image MappingEach algorithm having a different threshold score for matching.

Matched two different images of minutiae sets exploiting the relative distance measures pertaining to minutiae points in a set.

Results Matching Criterion:–

( match score > threshold score ) - Appreciable match ( match score < threshold score ) - Non - match

Matched Images

Match Score = 145 Threshold = 130 (Accepted)

Non Match

Match Score = 110 Threshold = 130 (Rejected)

Constraints

Rotation Variant. Quality of images should be good.

Difficulties …

High efficiency needed as the fields of application are related to security.

Future Work

Matching algorithms can be improved.By exploiting - minutiae orientation details. differentiating bifurcating and ending

minutiae’s. considering average ridge thickness

etc.

WorkbedPlatform – Windows

Tools – Microsoft Visual c++ , Matlab and Matlab addin for MS VC++.

Image Input - Scanner

References …

[1] A. K. Jain, L. Hong, S. Pankanti, R. Bolle, “An identity authentication system using fingerprints”, Proceedings of the IEEE, 85(9)(1997) 1365-1388.

[2] A. K. Jain, A. Ross, S. Prabhakar, “Fingerprint matching using Minutiae and Texture Features”.

[3] P. Bhowmick, A. Bishnu, B. B. Bhattacharya , M. K. Kundu, C. A. Murthy, T. Acharya, “Determination of Minutiae Scores for Fingerprint Image Applications”.

[4] Dario Maio and Davide Maltoni “Direct Gray-Scale Minutiae Detection In Fingerprints”.

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