FINGER PRINTS RECOGNITION SYSTEM

S.ANITHALAKSHMI 50409621003

Abstract The minutiae are ridge endings or bifurcations on the

fingerprints. They, including their coordinates and direction, are most distinctive features to represent the fingerprint.

Most fingerprint recognition systems store only the minutiae template in the database for further usage.

The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets.

This kind of minutiae-based fingerprint recognition systems consists of two steps, i.e., minutiae extraction and minutiae matching.

In the minutiae matching process, the minutiae

feature of a given fingerprint is compared with the minutiae template, and the matched minutiae will be found out.  

The template used for fingerprint recognition is further utilized in the matching stage to enhance the system’s performance.

These templates are been stored in the database for further processing.

Specific unique id is generated for each template stored.

The id is stored in the database along with the template locations.

Next is the verification process where you need to provide the id for comparison.

The given id is being compared with the id’s in the database and matched with the corresponding template of that id.

A message is being displayed on successful matching along with the score.

Otherwise an failure message is displayed indicating match not found.

In the identification stage we need not specify an id. Click on the Identify button which searches for any match and provides the corresponding result.

The database records can also be deleted through the application.

Auto extract and Auto identify check boxes can be selected if need which automatically extracts and identifies the matching image

It is also possible to load an image and compare it with the database images.

The colors of the template extraction can also be changed.

ORGANIZATIONAL PROFILE ORATOR SOLUTIONS

The name “Orator”– A Good Speaker, One who speaks well in public. A good speaker has lot of followers. Like that, We people are good speaker by our work. We satisfy our clients by our services. And we are here to provide Intelligent

SKILLS Banking & Real Estate Finance & Insurance

Hospital & Health care Industry Web Applications Office Automation Manufacturing Retailing

o Hospital and health care industry

o Web Applications

o Office Automation

o Manufacturing Retailing

HARDWARE REQUIREMENTS

Processor PENTIUM IV

RAM 128 MB

Hard Disk 40 GB

SOFTWARE REQUIREMENTS Browser Internet Explorer

Server side scripting Java

Database Ms-Access

Client side scripting HTML

Existing System The conventional methods to utilize minutiae

information are treating it as a point set and finding the matched points from different minutiae sets.

In existing system, the Sparse areas are not considered.

So the result may not obtain correctly. And the matching will be difficult to get an absolute result.

FingerPrint ViewRed Spots Minutiae

Proposed System

In Proposed system, we considered the sparse area and the fingerprint’s orientation field is reconstructed from minutiae and further utilized in the matching stage to enhance the system’s performance.

DESIGN OF THE PROJECT

Data Flow Diagram

Show the matching Score

Thump Impression

Extract

Particular Identify

Entire Verification

Database

If Matches Show the Particular ID No

Does Not Exist

False

True

If Matches

Does not match

False

True

Enroll

Module List

Minutiae Template

Minutiae Matching

Effective Region Estimation

Orientation Field Matching

Fusing Matching

Minutiae Template

Minutiae templates are a fraction of the size of fingerprint images, require less storage memory and can be transmitted electronically faster than images.

Minutiae Matching In this module we matches the fingerprint minutiae

by using both the local and global structures of minutiae.

The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood.

It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching.

The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching.

(a) X Person’s Fingerprint (b) X Person’s Fingerprint for

verification

Matching Stage

Effective Region ESTIMATION

we can extract the effective region by finding the smallest envelope that contains all the minutiae points. For an When only having minutiae feature, we can extract the effective region by only using minutiae illustration. Here, we put the original image together for the convenience to give a visual sense.

Estimation of Effective Region

Orientation Field Matching To compare two fingerprints’ orientation field, the

first step is alignment of these two fingerprints. The alignment is mainly based on minutiae information . Here we choose the Hough transform based approach to finish the alignment due to its simplicity.

Hough Transform (HT) is one of the most common methods for detecting shapes (lines, circles, etc.) in binary or edge images. Its advantage is its ability to detect discontinuous patterns in noisy images, but it requires a large amount of computing power.

Fusing Matching

A variety of combination rules have been proposed. It has shown that matching accuracy can be improved by combining independent matchers using Neyman–Pearson rule. Here, we will also use Neyman–Pearson rule for the task.

Matching of test fingerprint with template is done in Neyman-Pearson rule. Two sets of minutiae are compared. If matching score is found, then the fingerprint is matched with template. Otherwise does not matched with the template.

Input And Output Screens

Main Page

Authentication

Load BMP Image

Extract Template

Enroll

Reports

Verify

Verify

Identify

Clear Database

Clear Log

Benefits of the project

The project can be used for security purposes.

E.g. : Attendance ,

voter registration,

Crime Investigation,

forensic fingerprint searching.

CONCLUSION Orientation field is important for fingerprint

representation. In order to utilize the orientation information in automatic fingerprint recognition systems which only stores minutiae feature. So we can reduce the usage of memory and enhancing the performance of system.

We also utilize the reconstructed orientation field information into the matching stage. We can reduce the effect of wrongly detected minutiae. A fingerprint matching based on orientation field is used to combine with conventional minutiae matching for real applications.

THANK YOU