1. Automatic Fingerprint Verification Principal Investigator Venu Govindaraju, Ph.D. Graduate Students T.Jea, Chaohang Wu, Sharat S.Chikkerur

2. Conventional Security Measures  Token Based  Smart cards  Swipe cards  Knowledge Based  Username/password  PIN  Disadvantages of Conventional Measures  Tokens can be lost or misused  Passwords can be forgotten  Multiple tokens and passwords difficult to manage

3. Biometrics  Definition  Biometrics is the science of verifying and establishing the identity of an individual through physiological features or behavioral traits.  Examples  Physical Biometrics  Fingerprint  Hand Geometry  Iris patterns  Behavioral Biometrics  Handwriting  Signature  Speech  Gait

4. Fingerprints as biometrics  Established Science  Forensic institutions have used fingerprints to establish individual identity for over a century.  High Universality  Every person possesses the biometric  High Distinctiveness  Even identical twins have different fingerprints though they have the same DNA.  High Permanence  Fingerprints are formed in the foetal stage and remain structurally unchanged through out life.  High Acceptability  Fingerprint acquisition is non intrusive. Requires no training.

5. Introduction to Fingerprints Fingerprints can be classified based on the ridge flow pattern Fingerprints can be distinguished based on the ridge characteristics

6. Fingerprint Verification System Good quality Image Good quality Fingerprint Image Authentication Fingeprint Image Fingerprint Image Enhancement Minutiae Feature Extraction Matching methods Database Minutiae features Image Preprocessing Research at CUBS Includes Fingerprint Image Enhancement Minutiae Feature Extraction Point pattern matching

7. Fingerprint Image Enhancement Preprocessing Enhancement Feature Extraction Matching High contrast printTypical dry print Low contrast printTypical Wet Print

8. Traditional Approach Preprocessing Enhancement Feature Extraction Matching Local Ridge Spacing F(x,y) Projection Based Method Enhancement Frequency/Spatial Local Orientation (x,y) Gradient Method

9. Fourier Analysis Approach Preprocessing Enhancement Feature Extraction Matching FFT Analysis Energy Map E(x,y) Orientation Map O(x,y) Ridge Spacing Map F(x,y) FFT Enhancement

10. Fourier Analysis Fingerprint ridges can be modeled as an oriented wave Local ridge orientation Local ridge frequency

11. Fourier Analysis –Energy Map Preprocessing Enhancement Feature Extraction Matching Original ImageEnergy Map

12. Preprocessing Enhancement Feature Extraction Matching Original ImageLocal Ridge Frequency Map Fourier Analysis – Frequency Map

13. Preprocessing Enhancement Feature Extraction Matching Original ImageLocal Ridge Orientation Map Fourier Analysis-Orientation Map

14. Preprocessing Enhancement Feature Extraction Matching Original ImageEnhanced Image Fourier Domain Based Enhancement

15. Enhancement Results

16. Feature Extraction Methods Preprocessing Enhancement Feature Extraction Matching Thinning-based Method Thinning produces artifacts Shifting of Minutiae coordinates Direct Gray-Scale Extraction Method Difficult to determine location and orientation Binarized Image is noisy.

17. Chaincoded Ridge Following Method Preprocessing Enhancement Feature Extraction Matching

18. Minutiae Detection  Several points in each turn are detected as potential minutiae candidate  One of each group is selected as detected minutiae.  Minutiae Orientation is detected by considering the angle subtended by two extreme points on the ridge at the middle point. Preprocessing Enhancement Feature Extraction Matching

19. Pruning Detected Minutiae  Ending minutiae in the boundary of fingerprint images need to be removed with help of FFT Energy Map  Closest minutiae with similar orientation need to be removed Preprocessing Enhancement Feature Extraction Matching

20.  Pure localized feature  Derived from minutiae representation  Orientation invariant  Denote as (r 0, r 1, δ 0, δ 1, )  r 0, r 1 : lengths of MN 0 and MN 1  δ 0, δ 1 : relative minutiae orientation w.r.t. M  : angle of N 0 MN 1 Secondary Features Preprocessing Enhancement Feature Extraction Matching

21. Dynamic Tolerance Areas  Tolerance Area is dynamically decided w.r.t. the length of the leg.  Longer leg: Tolerates more distortion in length than the angle.  Shorter leg: tolerates less distortion in length than the angle. A B O Preprocessing Enhancement Feature Extraction Matching Dynamic tolerance Dynamic Windows

22. Feature Matching Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established.

23. OD=0.7865° Validation Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established.

24. Minutia Matching Preprocessing Enhancement Feature Extraction Matching 1.For each triangle, generate a list of candidate matching triangles 2.To recover the rotation between the prints. Find the most probable orientation difference 3.Apply the results of the pruning and match the rest of the points based on the reference points established

25. Data Sets Fig(a) Sensors and technology used in acquisition Fig(b) Paired fingerprintsFig(c) Database sets

26. Preliminary Results Min Total Error = 0.00% EER = 0.0% FRR at 0 FAR = 0.0% FAR FRR Threshold

27. Thank You http://www.cubs.buffalo.edu