This document describes a fingerprint recognition system that uses minutiae-based matching. It extracts minutiae features like ridge endings and bifurcations from fingerprints. These minutiae templates are stored in a database along with unique IDs. The system then performs verification by comparing a given fingerprint's minutiae to templates in the database. It also allows identification by searching the entire database for any matching templates without an ID. The proposed system aims to improve matching performance by reconstructing fingerprints' orientation fields from minutiae and incorporating this additional information into the matching process.

1. FINGER PRINTS
RECOGNITION SYSTEM
S.ANITHALAKSHMI
50409621003

2. 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.

3.  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.

4.  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.

5.  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.

6.  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.

7. 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

8. o Hospital and health care industry
o Web Applications
o Office Automation
o Manufacturing Retailing

9. HArDWArE rEQUIrEMENts
 Processor PENTIUM IV
 RAM 128 MB
 Hard Disk 40 GB

10. sOFtWArE rEQUIrEMENts
 Browser Internet Explorer
 Server side scripting Java
 Database Ms-Access
 Client side scripting HTML

11. 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.

12. FingerPrint View
Red Spots Minutiae

13. 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.

14. DESIGN OF THE PROJECT

15. 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

16. mODulE lIST
 Minutiae Template
 Minutiae Matching
 Effective Region Estimation
 Orientation Field Matching
 Fusing Matching

17. 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.

18. 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.

19. (a) X Person’s Fingerprint (b) X Person’s Fingerprint for
verification
Matching Stage

20. 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.

21. Estimation of Effective Region

22. 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.

23. 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.

24. INPuT aND OuTPuT SCREENS

25. Main Page

26. Authentication

27. Load BMP Image

31. Extract Template

32. Enroll

41. Reports

43. Verify

45. Verify

47. Identify

48. ClearDatabase

49. ClearLog

50. Benefits of the project
 The project can be used for security purposes.
 E.g. : Attendance ,
voter registration,
Crime Investigation,
forensic fingerprint searching.

51. 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.

52. THANK YOU