1. EE 7740 Fingerprint Recognition

2. Bahadir K. Gunturk2 Biometrics Biometric recognition refers to the use of distinctive characteristics (biometric identifiers) for automatically recognition individuals. These characteristics may be  Physiological (e.g., fingerprints, face, retina, iris)  Behavioral (e.g., gait, signature, keystroke) Biometric identifiers are actually a combination of physiological and behavioral characteristics, and they should not be exclusively identified into either class. (For example, speech is determined partly by the physiology and partly by the way a person speaks.)

3. Bahadir K. Gunturk3 Biometrics

4. Bahadir K. Gunturk4 Biometrics

5. Bahadir K. Gunturk5 Biometrics

6. Bahadir K. Gunturk6 Fingerprint Human fingerprints have been discovered on a large number of archeological artifacts and historical items.

7. Bahadir K. Gunturk7 Fingerprint In 1684, an English plant morphologist published the first scientific paper reporting his systematic study on the ridge and pore structure in fingerprints.

8. Bahadir K. Gunturk8 Fingerprint

9. Bahadir K. Gunturk9 Fingerprint A fingerprint image may be classified as  Offline: Inked impression of the fingertip on a paper is scanned  Live-scan: Optical sensor, capacitive sensors, ultrasound sensors, … Critical parameter are: Resolution, area, contrast, noise, geometric accuracy.

10. Bahadir K. Gunturk10 Fingerprint The fingerprint pattern exhibits different types of features. At the global level, the ridge line flow has one the following patterns. Singular points are sort of control points around which a ridge line is “wrapped”. There are two types of singular points: loop and delta. However, these singular points are not sufficient for accurate matching.

11. Bahadir K. Gunturk11 Fingerprint At the local level, there different local ridge characteristics. The two most prominent ridge characteristics, called minutiae, are:  Ridge termination  Ridge bifurcation At the very-fine level, intra-ridge details (sweat pores) can be detected. They are very distinctive; however, very high-resolution images are required. Bifurcation Termination

12. Bahadir K. Gunturk12 Example Matching is not easy due to: displacement, rotation, partial overlap, nonlinear distortion, changing skin condition, noise, feature extraction errors, etc.

13. Bahadir K. Gunturk13 Example There are many “ambiguous” fingerprints, whose exclusive membership cannot be reliably stated even by human experts.

14. Bahadir K. Gunturk14 Fingerprint Recognition Approaches Correlation-based matching: Intensity based correlation between the fingerprint images are computed. Minutiae-based matching: Minutiae are extracted from two fingerprints and stored as sets of points in the 2D plane. Matching is done based on minutiae pairings. Ridge feature-based matching: Local orientation and frequency of ridges, ridge shape, texture, etc are used for matching.

15. Bahadir K. Gunturk15 Orientation Image Orientation image shows the local orientation of ridges. The length of each element is proportional to its reliability.

16. Bahadir K. Gunturk16 Singularity and Core Detection Poincare index

17. Bahadir K. Gunturk17 Singularity and Core Detection Poincare index

18. Bahadir K. Gunturk18 Singularity and Core Detection

19. Bahadir K. Gunturk19 Singularity and Core Detection

20. Bahadir K. Gunturk20 Singularity and Core Detection

21. Bahadir K. Gunturk21 Singularity and Core Detection The straight lines normal to the ridges identify the “core”. (Use Hough transform to determine its coordinate.) The core is used as a registration point for fingerprints.

22. Bahadir K. Gunturk22 Minutiae Detection Binarize the image (using global thresholding, local thresholding, etc.) Apply thinning (by, for example, using morphological operations) to get the skeleton image. Analyze the neighborhood of each pixel in the skeleton image.

23. Bahadir K. Gunturk23 Minutiae Detection Minutia detection may be followed by post-processing to remove false minutiae structures.

24. Bahadir K. Gunturk24 Fingerprint Matching

25. Bahadir K. Gunturk25 Fingerprint Matching

26. Bahadir K. Gunturk26 Fingerprint Matching

27. Bahadir K. Gunturk27 Fingerprint Matching

28. Bahadir K. Gunturk28 Fingerprint Matching

29. Bahadir K. Gunturk29 Pre-alignment Computational complexity of previous approach might be high. It is a good idea to roughly align fingerprints: Find the core Find the average ridge orientation on the left and right sides of core Rotate fingerprint around the core such that the difference between the left and the ridge orientations are minimum.

30. Bahadir K. Gunturk30 Performance Comparison Fingerprints [FVC 2002] False reject rate: 0.2% False accept rate: 0.2% Face [FRVT 2002] False reject rate: 10% False accept rate: 1% Voice [NIST 2000] False reject rate: 10-20% False accept rate: 2-5%

31. Bahadir K. Gunturk31 Performance How to improve Fingerprint enhancement Estimating deformations Multiple matchers & combine results Multimodel biometrics

32. Bahadir K. Gunturk32 Fingerprint Matching