A Systematic Approach For Feature Extraction in Fingerprint Images

1. Sharat Chikkerur, Chaohang Wu, Venu Govindaraju {ssc5,cwu3,govind}@buffalo.edu A Systematic Approach For Feature Extraction in Fingerprint Images

2. Abstract • A new enhancement algorithm based on Fourier domain analysis is proposed. • Fourier analysis is used extract orientation, frequency and quality map in addition to doing enhancement. • The enhancement algorithm uses full contextual information and adapts radial and angular extents based on block properties. • A new feature extraction algorithm based on chain code analysis is presented. • An objective metric is used to evaluate the efficiency of the feature extraction.

3. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

4. Motivation : Enhancement • Anisotropic filter (Greenberg et.al, Yang et.al) • Very fast but cannot handle creases, wide breaks and poor qualityimages • Pseudo Matched filtering (Wilson, Grother Candela et. al) • Increases SNR but can lead to artefacts due to isotropic filtering. • Directional Filtering (Sherlock,Monro et. al.) • Very robust even near regions of high curvature but marked by large storage requirements. Frequency of ridges is assumed to be constant. • Gabor filter bank(Hong et. al) • Filter has optimal joint directional and frequency resolution but does not handle high curvature regions well due to block wise approach. Angular and radial bandwidths are constant. • Proposed approach • A single algorithm is used for contextual analysis and enhancement. • Utilized full contextual information. Adapts both frequency and angular bandwidth based on block properties. • Adapts to high curvature regions reducing blocking artifacts. • However, using full contextual information leads to processing complexity.

5. Qualitative Comparison : Feature Extraction • MINDTCT,NIST NFIS, (Garris et. al) • The algorithm is extremely fast. • Greedy approach to minutia detection leads to false positives.Extensive post processing is required to eliminate false positives • Adaptive Flow Orientation technique (Ratha et. al.) • Is capable of correcting breaks in the rides and is robust to noise. • Peak detection leads to false positivies in regions of poor ridge constrast.Also, thinning and morphological post processing shift minutia location. • Direct Gray Scale Ridge Following (Maio and Maltoni) • Does not have errors introduced due to binarization and has low computational complexity. • Cannot handle poor contrast prints and images with poor ridge structure. • Proposed method • Enhancement reduces spurious and missing minutiae. The locations of the minutiae are preserved during detection. • Contour based extraction is sensitive to binarization and enhancement errors.

6. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

7. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction

8. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image SNR is increased using Pseudo Matched filtering [Wilson et. Al, 1994], k = 0.15 is used to reduce artifacts Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction

9. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image The image is divided into blocks and Fourier analysis is done on each of them. The analysis produces orientation, frequency, angular bandwidth and quality maps [proposed] Binarization Minutiae Detection Contour Extraction

10. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image Each block is filtered using a orientation and frequency selective filter [Sherlock and Monro, 1994] with the given bandwidth Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction

11. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction The enhanced image is binarized using an locally adaptive algorithm

12. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction Contours of the ridges are extracted and traced consistently in a counter clockwise direction[Govindaraju et. al, 2003]

13. Overview of the proposed method Enhancement Contextual Filtering Fourier Analysis Preprocessing Gray Level Image Feature Extraction Feature Extraction Binarization Minutiae Detection Contour Extraction Minutiae are detected as points with 'signficant' turns in the contour. Vector products are used to quanity the turns

14. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

15. Local ridge orientation Local ridge frequency Surface Wave Model

16. Validity of the model • With the exception of singularities such as core and delta, any local region of the fingerprint has consistent ridge orientation and frequency. • The ridge flow may be coarsely approximated using an oriented surface wave that can be identified using a single frequency f and orientation . • However, a real fingerprint is marked by a distribution of multiple frequencies and orientation.

17. Obtaining block parameters • To obtain the dominant ridge orientation and frequency a probabilistic approximation is used • We can represent the Fourier spectrum in polar form as F(r, )The power spectrum is reduced to a joint probability density function using • The angular and frequency densities are given by marginal density functions ,

18. Obtaining block parameters (contd.) • The dominant ridge orientation is obtained using • The dominant frequency can be estimated using the expected value of the frequency density function, • The quality is assumed to be proportional to the strength of the ridge flow and is estimated using

19. Fourier Analysis –Energy Map Original Image Energy Map

20. Fourier Analysis – Frequency Map Original Image Local Ridge Frequency Map

21. Fourier Analysis-Orientation Map Original Image Local Ridge Orientation Map

22. Fourier Analysis : Angular Bandwidth

23. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

24. Fourier Domain Based Enhancement Contextual Filter Enhanced Image Original Image

25. Additional Enhancement Results

26. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

27. Determination of Turn Points • When the ridge contours are traced in a counter clockwise direction, minutiae are encountered as points with significant turn. • Types of turn points: left(ridge),right(bifurcation) • S(Pin, Pout) = S( )=S(x1y2 –x2y1) • Pin : Vector leading into the candidate point • Pout: Vector leading out of the point of interest • S(Pin, Pout) >0 indicates left turn, S(Pin, Pout) <0 indicates right turn • Significant turn can be determined by ( )=x1y1 + x2y2 < T

28. Turn points (a) Potential minutia location; (b) Determination of turn points

29. Post processing • Feature Extraction errors • Missing minutiae • Spurious minutiae • Spurious minutia can be removed using post processing • Heuristic rules: • Merge minutiae that are a certain distance of each other and have similar angles • Discard minutiae whose angles are inconsistent with ridge direction • Discard all border minutia • Discard opposing minutiae within certain distance of each other

30. Example Result

31. Outline • Related Previous Work • Overview of the proposed method • Fourier Analysis • Fingerprint Image Enhancement • Feature Extraction • Performance Evaluation • Conclusion

32. Quantitative Analysis • Test Data • 150 prints from FVC2002(DB1) were randomly selected for evaluation. • Ground truth was established using a semi automated truthing tool. • Results compared using NIST NFIS open source software. • Metrics • We use feature extraction metrics proposed by Sherlock et. Al • Sensitivity: Ability of the algorithm to detect true minutiae • Specificity : Ability of the algorithm to avoid false positives • Additional Metrics • Flipped : Minutiae whose type has been exchanged

33. Quantitative Analysis : Results • Examples

34. Quantitative Analysis : Results Summary results • Count TP(ANSI) > proposed : 40 of 150 • Count E(ANSI) < proposed : 40 of 150 Sensitivity distribution Overall statistics

35. Conclusion • A new effective enhancement algorithm based on Fourier domain analysis is proposed • A single algorithm is used to derive orientation, frequency, angular bandwidth and quality maps • A new feature extraction algorithm based on chain code contour analysis is presented • Heuristic rules specific to the feature extraction algorithm has been derived • The algorithm is evaluated using an objective metric

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

37. Related Previous Work: Enhancement • Spatial Domain • Anisotropic filter (Greenberg et.al, Yang et.al) • Uses a locally adaptive kernel • Blurs along the ridge direction. Increases the discrimination between ridges and valleys along the perpendicular direction. • Frequency Domain • Pseudo Matched filtering (Wilson, Grother Candela et. al) • The Fourier transform of the block is multiplied by its power spectrum raised to a power of k • Directional Filtering (Sherlock,Monro et. al.) • The image is decomposed into a set of eight directional responses using a bank of directionally selective filters. The frequency is assumed constant. • The enhanced image is obtained by composing the filter responses using the local orientations. • Gabor filter bank(Hong et. al) • The image is enhanced by using a Gabor filter bank • Gabor fillters have the optimum orientation and frequency resolution.

38. Related Previous Work: Feature Extraction • Binarized Images • MINDTCT, NIST NFIS, (Garris et. al) • An oriented grid is placed at each pixel and the projection sums are taken at each row. The pixel is assigned 0 if the projections sum at the center row is less than average, otherwise the pixel is assigned 1 • The minutiae are detected using structural rules. • Adaptive Flow Orientation technique (Ratha et. al.) • Orientation of each 16x16 block is determined by computing the gray level projections at various angles. The projection along a scan line perpendicular the ridge direction has maximum variance. • The image is binarized by detecting the peaks along this scan line. • The minutiae are detected using the thinned image • Gray Scale Image • Direct Gray Scale Ridge Following (Maio and Maltoni) • A set of starting points are chosen by superimposing a grid on the image • The ridge is traced from each starting point until a bifurcation or ridge ending is found. • A labelling strategy is used to preven traversing the same ridge twice.