Fingerprint Verification System

1. Authentication Fingeprint Image Image Preprocessing Fingerprint Image Enhancement Minutiae Feature Extraction Matching methods Good quality Image Good quality Fingerprint Image Minutiae features Database Fingerprint Verification System

2. Fingerprint Segmentation Separation of fingerprint area (foreground) from the image background • Traditional methods use block level features • Local histogram of ridge orientation • Gray-level variance • Magnitude of the gradient in each image block • Gabor feature • My new method- point feature

4. Fingerprint Feature-Minutiae

5. Traditional Feature Detection Algorithm- Binarization-Thinning • binarization followed by thinning step, the width of the ridges reduced to one pixel • Location of minutiae points in the skeleton image • number of neighbor black pixels at a point of interest in a 3 X 3 window • crossing number ( ending: cn(p) =1, bifurcation: cn(p)=3, normal:cn(p) =2) • Thinning limitation: Aberrations and irregularity of the binary ridge boundaries have an adverse effect on the skeletons, leads to the detection of spurious minutiae

6. q Pin × Pout Pin Pout q Pout Pin (b) (c) Pin × Pout SA: Start Point of Pin Middle Point of SA and EB q EB: End Point Pout Minutiae Point (a) (d) Figure 8 Minutiae Detection (a) Detection of turning points, (b) & (c) Vector cross product for determining the turning type, (d) Determining minutiae direction New Minutiae Detection Method C F B Bifurcation Start

7. Post processing (Elimination of False Minutiae in the Image Boundary )

8. Determination of Turn Points • The ridge contours of fingerprint images can be consistently traced in a counter-clockwise fashion • Two types of turn points: left and right • S(Pin, Pout) = 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 • Angle between Pin and Pout

9. IMAGE QUALITY MODELING -Proposed Limited Ring FFT Spectral Measures the spectrum in polar coordinates, S(r, θ) For each direction θ, Sθ( r ) – the spectrum behavior along a radial direction from the origin • For each frequency r, Sr(θ) – the spectrum behavior along a circle centered on the origin

12. Enhancement in High-curvature region of Fingerprint Image (2) • Calculate the Gradients Gx, Gy • Calculate variances (Gxx, Gyy) and cross-covariance (Gxy) of Gx and Gy • Calculate coherence map • sqrt((Gxx-Gyy)^2+4*Gxy^2)/(Gxx + Gyy) • Find the minimum coherence value in ROI • Add 0.1+ minimum (Coh) • Get the high curvature regions with region property like centroid or bounding box

13. Enhancement Results

14. Enhancement results Core Delta

15. Enhancement results