1. FINGER PRINT RECOGNITION USING MINUTIAE EXTRACTION FOR BANK LOCKER SECURITY
Presented by
J.VENKATA SUMAN
ECE DEPARTMENT
GMRIT, RAJAM

2. ABSTRACT
To reach the growing need to identify a person for security , Our paper develops an accurate, fast and very efficient bank locker system using fingerprint verification technique.
We propose a system in which finger print verification is done by using extraction of minutiae technique which comprises of Image Preprocessing, Feature Extraction and Feature Matching.

3. APPROACHES FOR FINGER PRINT RECOGNITION
Minutiae Based
Image Based
Our paper is finger print recognition through Minutiae based approach.
Finger print recognition through minutiae based approach is very convenient and reliable way to verify the person’s identity.

4. INTRODUCTION Finger print comprises mainly Ridges and Furrows.
The ridges are the dark areas of the fingerprint and the furrows or valleys are the white areas that exist between the ridges.
As these resemble with each other, Minutiae brings in concepts of Terminations and Bifurcations as they are easily detected by points that surround them.

5. INTRODUCTION CONT.

6. SYSTEM DESIGN

7. LOAD
A gray level fingerprint image from any drive can be loaded

8. IMAGE ENHANCEMENT Make image clearer.
Increase the contrast between ridges and furrows.
Connect the false broken points of ridges.
Keep a higher accuracy.

9. HISTOGRAM EQUALIZATION
It is used to enhance the contrast of images by transforming its intensity values.
To expand the pixel value distribution of an image.
The histogram after the histogram equalization occupies all the range from 0 to 255.
The visualization effect is enhanced or increased.
Noise is prevented from being amplified

10. Fig: Image enhancement by histogram equalization

11. FOURIER TRANSFORM
To connect some falsely broken points on ridges and to remove some spurious connections between ridges.
We can enhance each block by
g(x,y)= F-1 { F(u,v)×|F(u,v)|k }
Higher "k“ value improves the appearance of the ridges
Too high value of "k" can result in false joining of ridges.

12. Fig: Image Enhancement by Fourier transform

13. BINARIZATION
To transform the 8-bit Gray finger print image to a 1-bit image.
0 value for ridge 1 value for furrow.
Use locally adaptive method.
Transform a pixel value to 1 if the value is larger than the mean intensity value of the current block.

14. Fig: Binarization

15. IMAGE SEGMENTATION
Only a Region of Interest (ROI) is useful to be recognized for each fingerprint image.
To extract ROI - 2 operations are adopted OPEN and CLOSE
‘OPEN’ operation can expand images and remove peaks.
‘CLOSE’ operation can shrink images and eliminate small cavities.

16. Fig: Direction Estimation

17. Fig: ROI Extraction

18. MINUTIA EXTRACTION Ridge Thinning:
To eliminate the redundant pixels of ridges till the ridges are just one pixel wide.
Thinning does not change the location or orientation of minutiae points.

19. Fig: Thinning

20. MINUTIA MARKING
Normal ridge Bifurcation Termination
pixel

21. Fig: Minutiae Extraction

22. FALSE MINUTIA REMOVAL
False ridge breaks due to insufficient amount of ink and ridge cross connections due to over inking are not totally eliminated in the pre-processing stage.

23. D =( For each row sum up all pixels in the row whose value is 1)/row length
where D is the inter ridge width.
Procedure for removal of false minutiae:
Distance(bifurcation , termination) < D and the two minutiae are in the same ridge then remove both of them.(m1)
Distance(bifurcation , bifurcation) < D and the two are in the same ridge then remove both of them.(m2,m3)
Distance(termination ,termination) ~ D and their directions are co-incident and no other termination is located in between the two then remove both of them.(m4,m5,m6)
Two terminations are located on the same ridge with length less than D then remove the two terminations.(m7)

24. The removal of below shown false minutiae limits the maximum number of minutiae present in a thinned image to a pre-defined threshold.
Spike Bridge Hole
Break Spur ladder

25. Fig: False minutiae removal

26. MINUTIAE MATCH There are two consecutive stages:
One is alignment stage and the second is match stage.
1. Alignment stage:
Choose any one minutiae from each image, calculate the similarity of the two ridges.
If the similarity is larger than a threshold, transform each set of minutia to a new coordination system.

27. 2. Match Stage:
After we get two set of transformed minutiae points, we use the elastic match algorithm to count the matched minutiae pairs.
The final match ratio for two fingerprints is the number of total matched pair over the number of minutiae of the template fingerprint.
The score is 100 % ratio and ranges from 0 to 100.

28. Fig: Minutia based matching

29. Fig: Match the minutia of two images and display the score

30. CONCLUSION
This paper presents an overview of the different steps involved in the development of a Minutiae based Fingerprint identification and verification system for bank locker security. We have also proposed to design and development of a minutiae based AFIS.
The system developed is still a prototype version and needs improvements for decreasing the time spent during fingerprint processing and the reduction in the number of false acceptances and rejections made by the algorithm.

31. REFERENCES
“A Handbook Of Fingerprint Recognition”, Maltino, Maio, Jain And Prabhakar. Springer Press.
Pradeep M. Patil, Shekar R. Suralkar and Faiyaz B. Sheikh, “Rotation Invariant Thinning Algorithm to Detect Ridge Bifurcations for Fingerprint Identification,” IEEE International Conference on Tools with Artificial Intelligence, 2005.
“Digital Image Processing”, Gonzalez And Woods, Aw, Ma 1992.
L. Hong, "Automatic Personal Identification Using Fingerprints", Ph.D. Thesis, 1998.

33. conclusion