1. 1 Fingerprint Analysis and Representation Handbook of Fingerprint Recognition Chapter III Sections 1-6 Presentation by: Tamer Uz 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.

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

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

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

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

6. 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. 7 Introduction A Global Look Singular regions are commonly used for fingerprint classification:

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

9. 9 Introduction Local Look Ridge ending / ridge bifurcation duality

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

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

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

13. 13 Estimation of Local Ridge Orientation Averaging Gradient Estimates Averaging Gradient Estimates (Kass, Witkin 1987) d ij = [r ij.cos2θ ij, r ij sin2 θ ij ] d ij = [r ij.cos2θ ij, r ij sin2 θ ij ]

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

15. 15 Estimation of Local Ridge Orientation Effect of averaging Effect of averaging

16. 16 Estimation of Local Ridge Frequency

17. 17 Estimation of Local Ridge Frequency Simple Algorithm Simple Algorithm 1) 32x16 oriented window centered at [x i, y i ] 2) The x-signature of the grey levels is obtained 3) f ij is the inverse of the average distance To handle noise interpolation and/or low pass filtering is applied.

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

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

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

21. 21 Segmentation Example Segmentation Example Segmentation

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

23. 23 Singularity and Core Detection Poincare Method Poincare Method

24. 24 Singularity and Core Detection Poincare Method Poincare Method

25. 25 Singularity and Core Detection Poincare Method Poincare Method

26. 26 Singularity and Core Detection Poincare Method 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 singularitiesArch fingerprints do not contain singularities Left loop, right loop and tented arch fingerprints contain one loop and one deltaLeft loop, right loop and tented arch fingerprints contain one loop and one delta Whorl fingerprints contain two loops and two deltasWhorl fingerprints contain two loops and two deltas

27. 27 Singularity and Core Detection Methods based on local features Methods based on local features Orientation histograms at local levelOrientation histograms at local level IrregularityIrregularity

28. 28 Singularity and Core Detection Partitioning based methods Partitioning based methods

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

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

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

32. 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. 33 Outline Introduction Introduction Related Work Related Work Proposed Algorithm Proposed Algorithm Experimental Results Experimental Results Conclusion Conclusion

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

35. 35 Introduction General Stages of the Feature Extraction Process General Stages of the Feature Extraction Process PreprocessingPreprocessing Direction ComputationDirection Computation BinarizationBinarization ThinningThinning PostprocessingPostprocessing

36. 36 Related Work

37. 37 Proposed Algorithm

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

39. 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. G x (i,j) and G y (i,j) using 3x3 Sobel MasksCompute the gradient of the smoothed block. G x (i,j) and G y (i,j) using 3x3 Sobel Masks Obtain the dominant direction in the block using the following equation:Obtain the dominant direction in the block using the following equation: Quantize the angles into 16 directions.Quantize the angles into 16 directions.

40. 40 Proposed Algorithm 1.1 Computation of the Orientation Field

41. 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. 42 Proposed Algorithm 1.2 Foreground/Background Segmentation

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

44. 44 Proposed Algorithm 1.3 Ridge Segmentation

45. 45 Proposed Algorithm 1.3 Ridge Segmentation

46. 46 Proposed Algorithm 1.4 Directional Smoothing A 3x7 mask (containing all 1s) is placed along the orientation 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. 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. If the 1s are more than 25 percent of the mask area than the ridge point is retained.

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

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

49. 49 Proposed Algorithm 2) Minutiae Extraction

50. 50 Proposed Algorithm 2) Minutiae Extraction

51. 51 Proposed Algorithm 3) Post Processing Ridge breaks (insufficient ink or moist)Ridge breaks (insufficient ink or moist) Ridge cross-connections (over-ink, over-moist)Ridge cross-connections (over-ink, over-moist) BoundariesBoundaries

52. 52 Experimental Results Summary of the procedures Summary of the procedures

53. 53 Experimental Results Summary of the procedures Summary of the procedures

54. 54 Experimental Results Performance Evaluation Detected minutiae is compared with the ground truth (extracted by human experts)Detected minutiae is compared with the ground truth (extracted by human experts) L: Number of 16x16 windows in the input image P i : Number of minutiae paired in the i th window Q i : Quality factor of the i th window (good=4, medium=2, poor=1) D i : Number of deleted minutiae in the i th window I i : Number of inserted minutiae in the i th window M i : Number of ground truth minutiae in the i th window

55. 55 Experimental Results Performance Evaluation Base Line DistributionBase Line Distribution Generate same number of random minutiae in the foreground of (512x512) image Generate same number of random minutiae in the foreground of (512x512) image Calculate the GI. Calculate the GI.

56. 56 Experimental Results Performance Evaluation

57. 57 Conclusion Robust feature extraction based on ridge flow orientations Robust feature extraction based on ridge flow orientations Novel segmentation method Novel segmentation method An adaptive enhancement of the thinned image An adaptive enhancement of the thinned image Quantitative performance evaluation Quantitative performance evaluation The execution time must be substantially reduced The execution time must be substantially reduced