Fingerprint Analysis and Representation

1. Fingerprint Analysis and Representation Adaptive Flow Orientation based Feature Extraction in Fingerprint Images Handbook of Fingerprint Recognition Chapter III Sections 1-6 N.K. Ratha, S. Chen, A.K. Jain, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, 1995. Presentation by: Tamer Uz

2. Fingerprint Analysis and Representation Handbook of Fingerprint Recognition Chapter III Sections 1-6

3. Outline • Introduction • Estimation of Local Orientation • Estimation of Local Ridge Frequency • Segmentation • Singularity and Core Detection

4. Introduction Fingerprint Interleaved ridges and valleys Ridge width: 100μm-300 μm Ridge-valley cycle: 500 μm

5. Introduction A Global Look Singularities: In the global level the fingerprint pattern shows some distinct shapes • Loop ( ) • Delta (Δ) • Whorl (O)…Two facing loop

6. Introduction A Global Look Core: A reference point for the alignment. The northmost loop type singularity. According to Henry(1900), it is the northmost point of the innermost ridgeline. Not all fingerprints have a core (Arch type fingerprints)

7. Introduction A Global Look Singular regions are commonly used for fingerprint classification:

8. Introduction Local Look Minutia: Small details. Discontinuties in the ridges. (Sir Francis Galton)

9. Introduction Local Look Ridge ending / ridge bifurcation duality

10. Introduction Local Look Sweat Pores • High resolution images (1000 dpi) • Size 60-250 μm • Highly distinctive • Not practical (High resolution, good quality images)

11. Estimation of Local Ridge Orientation • Quantized map • Average orientation around indices i,j • Unoriented directions • Weighted (rij)

12. Estimation of Local Ridge Orientation • Simple Approach • Gradient with Sobel or Prewitt operators • Θij is orthogonal to the direction of the gradient Drawbacks: • Non-linear and discontinuous around 90 • A single estimate is sensitive to noise • Circularity of angles: Averaging is not possible • Averaging is not well defined.

13. Estimation of Local Ridge Orientation • Averaging Gradient Estimates (Kass, Witkin 1987) dij = [rij.cos2θij, rijsin2 θij]

14. Estimation of Local Ridge Orientation • Reliability (rij) • calculated according to variance or least sq. residue • Like detecting outliers and assigning low weights to them.

15. Estimation of Local Ridge Orientation • Effect of averaging

16. Estimation of Local Ridge Frequency

17. Estimation of Local Ridge Frequency • Simple Algorithm • 32x16 oriented window centered at [xi, yi] • The x-signature of the grey levels is obtained • fij is the inverse of the average distance To handle noise interpolation and/or low pass filtering is applied.

18. Estimation of Local Ridge Frequency • Other Algorithms • Mix-spectrum technique (Jiang, 2000) • Energy of 2nd and 3rd harmonics in the spectrum (Fourier) domain is imposed on the fundamental frequency. • Variation function technique (Maio Maltoni 1998a)

19. Estimation of Local Ridge Frequency • Example on Variation Function Tech.

20. Segmentation • Segmentation Methods • Orientation histogram in neighborhood. • Variance orthogonal to the ridge direction • Average magnitude of gradient in blocks • Threholding the variance of Gabor Filter (Band-pass) responces. • Classifying pixels as forground or background using gradient coherence, intensity mean and intensity vaience as features

21. Segmentation • Example Segmentation

22. Singularity and Core Detection • Singularity Detection Methods • Poincare method • Methods based on local characteristics of the orientation image • Partitioning based methods

23. Singularity and Core Detection • Poincare Method

24. Singularity and Core Detection • Poincare Method

25. Singularity and Core Detection • Poincare Method

26. Singularity and Core Detection • Poincare Method If we know the type of the fingerprint beforehand, false singularities can be eliminated by iteratively smoothing the image with the help of the following observation: • Arch fingerprints do not contain singularities • Left loop, right loop and tented arch fingerprints contain one loop and one delta • Whorl fingerprints contain two loops and two deltas

27. Singularity and Core Detection • Methods based on local features • Orientation histograms at local level • Irregularity

28. Singularity and Core Detection • Partitioning based methods

29. Singularity and Core Detection Core Detection: Core: North most loop type singularity • It is generally used for fingerprint registration • It needs to be found for the arches from scratch • Has to be validated for the others

30. Singularity and Core Detection • Core Detection Popular Algorithm (Wegstein 1982): • Orientation image is searched row by row • The sextet best fits a certain criteria is found and the core is interpolated • Accurate • Complicated and heuristic

31. Singularity and Core Detection • Core Detection Other idea: • Voting based line intersection

32. Adaptive Flow Orientation based Feature Extraction in Fingerprint Images N.K. Ratha, S. Chen, A.K. Jain, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, 1995.

33. Outline • Introduction • Related Work • Proposed Algorithm • Experimental Results • Conclusion

34. Introduction • This paper proposes a feature extraction method from fingerprint images. • Extracted features are minutiae (x,y,Θ) • Method: Extracting orientation field followed by segmentation and analysis of the ridges

35. Introduction • General Stages of the Feature Extraction Process • Preprocessing • Direction Computation • Binarization • Thinning • Postprocessing

36. Related Work

37. Proposed Algorithm

38. Proposed Algorithm 1)Preprocessing and Segmentation Goal: To obtain binary segmented ridge images. Steps: • Computation of orientation field • Foreground/background separation • Ridge segmentation • Directional smoothing of the ridges

39. Proposed Algorithm 1.1 Computation of the Orientation Field An orientation is calculated for each 16x16 block Steps: • Compute the gradient of the smoothed block. Gx(i,j) and Gy(i,j) using 3x3 Sobel Masks • Obtain the dominant direction in the block using the following equation: • Quantize the angles into 16 directions.

40. Proposed Algorithm 1.1 Computation of the Orientation Field

41. Proposed Algorithm 1.2 Foreground/Background Segmentation Variance of grey levels in the direction orthogonal to the orientation field in each block is calculated. Assumption: fingerprint area will exhibit high variance, where as the background and noisy regions will exhibit low variance. Variance can also be used as the quality parameter of the regions. High variance (high contrast): good quality Low variance (low contrast): poor quality

42. Proposed Algorithm 1.2 Foreground/Background Segmentation

43. Proposed Algorithm 1.3 Ridge Segmentation • Orientation field is used in each (16x16) window • Waveform is traces in the direction orthogonal to the orientation • Peak and the 2 neighbouring pixels are retained • The retained pixels are assigned with the 1 and the rest are assigned with 0.

44. Proposed Algorithm 1.3 Ridge Segmentation

45. Proposed Algorithm 1.3 Ridge Segmentation

46. Proposed Algorithm 1.4 Directional Smoothing • A 3x7 mask (containing all 1s) is placed along the orientation • The mask enables to count the number of “1”s in the mask area. • If the 1s are more than 25 percent of the mask area than the ridge point is retained.

47. Proposed Algorithm 2) Minutiae Extraction We are a few steps away from extracting the minutiae. • First ridge map is skeletonized. • Ridge boundary aberrations result • In hairy growths. • It is smoothed by using morphological binary “open” operator

48. Proposed Algorithm 2) Minutiae Extraction Morphological binary “open” operator http://documents.wolfram.com/applications/digitalimage/UsersGuide/Morphology/ImageProcessing6.3.html

49. Proposed Algorithm 2) Minutiae Extraction

50. Proposed Algorithm 2) Minutiae Extraction