Fingerprint Matching Techniques: Global vs. Local Minutiae Matching

1. Fingerprint MatchingChapter 4, sections 4.4-4.8Handbook of fingerprint recognition&Filterbank-Based Fingerprint MatchingJain A.K. Prabhakar S., Jonh L. and Pankanti S., “IEEE Trans. On Image Processing”, vol. 9, No. 5, 2005. Alireza Tavakkoli

2. Fingerprint Matching Global vs Local Minutiae Matching. Dealing with Distortion. Ridge Feature-based Matching Techniques. Comparing the Performance. Filterbank-based Matching: Motivation Filter-based feature extraction: Reference point location. Filtering Feature vectors Matching Experimental results Outline

3. Global vs. Local Minutiae Matching • Trade offs: • Simplicity, low cost, high distortion tolerance. • High distinctiveness. • [Hrechak and McHugh (1990)]: • Eight dimensional feature vector: • Minutiae: dots, ridge endings, ridge bifurcations, islands, spurs, crossovers, bridges and short ridges. • Invariant to fingerprint alignments. • Practical applicability!!!!!!

4. Global vs. Local Minutiae Matching • Chen and Kuo (1991), Wahab, Chin and Tan (1998): • Enriched local structures proposed by Harchak and McHugh in 1990. • Distance • The ridge count • Relative orientation of each surrounding minutiae with the central one. • Angle between orientation of the line connecting each minutiae to central one and its orientation. • Comparing local structures by correlation or tree-matching. • Fan, Liu and Wang(2000): (Geometric Clustering) • Each cluster  rectangular bonding box. • Using a fuzzy bipartite weighted graph matching. • Willis and Myers (2001): • Minutiae counting in a dart board pattern of wedges and ridges. • Partially invariant to rotation and translation.

5. Global vs. Local Minutiae Matching • [Jiang and Yau, Ratha et. al. (2000)]: (Using both methods advantages) • Fast local matching for recovering alignments. • Consolidation stage. JiangRatha

6. Zhang and Wang (2002): Using Core points speed up the initial local structure matching. Lee, Choi and Kim (2002): Using more minutiae pairs: Guide the consolidation step. Robustness Normalization. Variants of the 2 Stage Algorithm

7. More Local Minutiae Matching Methods • Maio and Maltoni (1995) and Kovac-Vajna (2000): • Enhancement and accurate minutiae extraction only on template. • Extraction of minutiae template T. • Locally checking correspondence in verification stage. • Maio: • Gray level minutiae extraction. • Locally tracking the ridges in verification for finding correspondence.

8. Kovac Algorithm • Kovac: • 16x16 neighborhood of minutiae in T correlated by I list of candidate positions. • Triangular matching: • Start with 2 minutiae in T and candidate positions in I. • Expanding the list by adding a pair of minutiae and candidate. • Consolidation: • Checking the correspondence of gray scale profiles between every pair. • 1) Ridge count. • 2) Dynamic time warping (Handle small perturbations).

9. Dealing with Distortion • One of the most critical intra-class variability. (NIST 24) • Mechanical force sensor  less distortion • Automatic detection of distortion from videos. • Distortion-tolerant matchers: • Both of the above solutions are difficult to implement in commercial sensing systems.

10. How to deal with distortion? • Relaxing spatial relationships between minutiae: • Global matching techniques: • Tolerance boxes (spheres): • High distortion  larger Boxes  high false match • Polar coordinate boxes (Jain (97) and Luo (2000)): • Edit distance for matching pre-aligned minutiae. • Size of boxes increase by the distance from center. • Kovac method: • Triangular matching can tolerate large global distortions. • Adding this small differences may be large!!!! • Non of the above explicitly address the problem!

11. Dealing with Distortion • Almansa and Cohen (2000): • A 2D warping algorithm (mapping FP patterns): • Controlling warping by minimizing and energy function. • Two minutiae spatially coincide. • Penalty term  increasing by the irregularity of the warping. • Two step iterative algorithm to minimize energy. • Problem with convergence!!!

12. Dealing with Distortion • Bazen and Gerez (2002): • Smoothed mapping between template and input minutiae. • Algorithm: • Initially computing minutiae through a local approach and consolidation step. • Reduction of the size of tolerance box • Use of a thin spline model to deal with non-linear distortion. • Locally moving minutiae in input image to best fit the template minutiae, iteratively. (According to the model smoothness constrains) • Significant improvements achieved.

13. Normalization to canonical form Senior and Bolle (2001)

14. Normalization Techniques • Lee Chi and Kim (2002): • Normalization during the matching stage: • Normalization according to local ridge frequency. • Distortion  increase in distance between minutiae  local ridge frequency decreases  Normalization can compensate for that. • Problem: • Far apart ridges  normalization may have higher distortion errors than the distortion itself.

15. Modeling Skin Distortion [Maio]

16. Distortion Recovery

17. Ridge Feature-based Matching Techniques • Why? • Difficulty in reliable minutiae extraction from poor quality images. • Time consuming. • Use of additional features increases the accuracy and robustness. • Alternative features: • Size and silhouette. (unstable) • Singularities. (unstable) • Spatial relationship. (tree grammars, incremental graph matching) • Shape features. (1D signature from 2D, used with minutiae-based) • Global/local texture. (Texture properties from ridge lines) • Sweat pores. (Very discriminative but expensive) • Fractal features.

18. Fingerprint Texture Analysis • Analyzing texture in furrier domain:(Coetzee and Botha (93) and Willis and Myers (2001)) • Spatial fingerprint texture Almost constant in frequency domain. • Small deviations from the dominant frequency  minutiae!! • Wedge-ring detector. • Accumulating the harmonic of individual regions. • Global texture analysis  all regions into one measurement  Loss of spatial information. • Filterbank-based Analysis of Fingerprint: (Jain (2000)) • Topic of next talk (!).

19. Comparing Performance • Various fingerprint matching techniques. • Which one is the best algorithm? • Performance involves a Trade off among different measures. • Performance relates to difficulty of the benchmark  lack of a global one. • Before FVC NIST Databases  not good for live-scan. • NIST 4, 10, 14: Rolled inked impressions. • NIST 24 : Videos. • NIST 27 : Latent fingerprints. • FVC2000/02 : (can be found on the DVD of the book)

20. Typical Mistakes • Using the same datasets for trainig, validation and testing. • Computing performance on very small dataset. • Cleaning the dataset by removing rejected or misclassified samples. • Claiming better classification while using different datasets. • Hiding the weak points of an algorithm/ Documenting its failures.

21. Second Talk Filterbank-Based Fingerprint Matching Jain A.K. Prabhakar S., Jonh L. and Pankanti S. “IEEE Trans. On Image Processing”, vol. 9, No. 5, 2005.

22. Outline • Motivation • Filter-based feature extraction: • Reference point location. • Filtering • Feature vectors • Matching • Experimental results

23. Introduction • Extraction and explicit detection of complete ridge structures!??? • Use of components of rich discriminatory information. • Local ridge structures. • Matching fingerprints with different number of registered minutiae.

24. Overview • Single reference point: • Assuming the vertical alignment. • Rotation invariance can be achieved by a cyclic rotation of the extracted feature values. • Tessellation of region of interest around reference point. • Filtering the region of interest in 8 direction using Gabor filter-banks. • Computation of the Average Absolute Deviation (AAD) of gray values in each sector. • Generation of the “Finger Code”.

25. Overview

26. Reference Point Location • Using conspicuous landmarks to locate reference point. • Point of maximum curvature of concave ridges.

27. Reference Point Location (Contd.) • Multiple resolution analysis of orientation map: • Handling noise in poor quality images: • Using large neighborhoods. • Accurate localization: • Sensitive to local variations. • Estimation of Orientation Field.

28. Least Square Orientation Estimation • Divide Image into wxw blocks. • Compute gradient at each pixel. • Estimate the local orientation at center of each block.

29. Reference Point Location Algorithm • Estimate the orientation field described above. • Smooth the orientation field in a local neighborhood: • Use a continuous vector field. • Compute the sine component of the smoothed orientation field, (E) • Initialize a label image, (A).

30. Reference Point Location Algorithm • For each pixel in the E, integrate the values of region RI and RII and compute: • Find maximum of A and assign its coordinate to core. • Perform algorithm for a fixed number of times with less window sizes.

31. Localization of Core Point

32. Tessellation of Region of Interrest

33. Filtering • Gabor filters: • Remove noise. • Preserve true ridge and valley structures • Provide directional information. • Minutiae: • Anomaly in local parallel ridges.

34. Filtering Stages • Normalization: • Even Symmetric Gabor Filter: • Mask 32x32. • Ferq. = 1/k • Angels:

35. Filtering Results

36. Feature Vector • Average Absolute Deviation:

37. How Discriminatory?

38. Matching • Euclidian distance. • Translation Invariance: • Reference Point • Rotation Invariance: • Approximated by cyclic rotation of Finger Codes. • Generating 11.25 degree rotated image in registration stage.

39. Experiments • Database 1: (MSU-DBI) • 167 subjects. • Digital Biometrics’ optical sensor. • Image size: 508x480 • 35% women. • 46.5% under 25. • 50.51% between 25 and 50. • 2.5% older than 50. • Two impressions taken from four finger. • A second round of collection after 6 weeks. • Total database size: 2672 images. • Live feedback at collection time  well centered images. • Distortion in data collected after 6 weeks  Challenging.

40. Experiments • Database 2: (NIST 9 Vol. 1 CD 1) • 1800 images. • 900 different fingers. • 832x768

41. MSU-DBI Rejected: 100(4%) Why? Ref point at corner. Poor Quality. (dryness) NIST 9 Rejected: 100 (5.6%) Why? The same reasons. Experiments

42. Genuine and Imposter Probabilities

43. Experiments

44. ROC curve (MSU-DBI)

45. ROC Curve (NIST9)

46. Observations • Most of false accepts are among the same type. • Good for indexing. • Captures the discriminatory information. • Good for combining with minutiae. • Combination by Neyman-Pearson Rule.

47. Neyman-Pearson Rule

48. Questions?