Digital Image Processing - ( monsoon 2003) FINAL PROJECT REPORT

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

2. 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.

3. 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.

4. Algorithm This particular method of fingerprint matching consists mainly of six stages …. • Image Enhancement, • Ridge extraction • Binarization • Thinning • Minutiae extraction • Post processing.

5. 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.

6. 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.

7. 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.

8. Minutiae Detection Once 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.

9. Building a minutiae skeleton • Set of distances between ridge bifurcating and ridge ending minutiaes. • Distribution of minutiaes. • Orientation of minutiaes.

10. 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.

11. Overall Process Image Enhancement and Ridge Detection Binarization Thinning Sensor Fingerprint Database Minutiae Extraction Matching Result

12. This is all what we proposed how we do it …..

13. Enhancement • We have achieved Appreciable enhancement using Gabor filters. • Gabor filters have both frequency selective and orientation selective properties, so it enhance the image and preserves the true ridge/valley structures. • Binarization of image using threshold values.

14. Thinning • Reducing width of ridges to a ‘single’ pixel. • Morphological thinning. • Doing erosion till no further change occurs in the image. • Removing pixels not satisfying m-connectivity to eliminate multiple paths.

15. Minutiae Detection • Next step is to detect Minutiae in the image. • Mask of 3x3 is made to check the neighbours of a pixel. • Then another mask having binary values is used to check the direction of the ridge at that point. • All the minutiae’s have 2 properties :- • Type of minutiae ( termination or bifurcation ) • Orientation • We have achieved quite efficient results in detecting all the minutiae points. • Removal of False minutiae points.

16. Matching of minutiae sets • Algorithms Used • Relative Distance Matching :– Finding the nearest similar minutiae and giving a score depending upon the distance. • Using Quad Tree :– exploiting distribution property. • Image Mapping :– sliding one thinned image upon another and finding the max. score obtained. • Each algorithm having a different threshold score for matching. • Results Matching Criterion:– • ( match score > threshold score ) - Appreciable match • ( match score < threshold score ) - Non - match

17. Results

18. Matched Images Match Score = 145 Threshold = 130 (Accepted)

19. Non Match Match Score = 110 Threshold = 130 (Rejected)

20. Constraints • Rotation Variant. • Quality of images should be good. Difficulties … High efficiency needed as the fields of application are related to security.

21. Future Work • We can improve the results by - • Enhancing the image by completing ridges. • Considering average ridge thickness. • A more robust algorithm for minutiae matching.

22. Applications … • Fingerprint Matching • Identifiers, Personal Identification Systems, Tracing criminals. • Fingerprint Verification Secure access, digital signatures etc.

23. Workbed Platform – 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”.

24. Thx