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

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