1. Coarse Classification
Fingerprints can be divided into 6 basic classes (some systems use other classes)
Arch
Tented Arch
Whorl
Right loop
Left Loop
Double Loop

4. Orientation Field
The orientation field gives the ridge direction at each point in the image

5. Identifying Core and Delta
The orientation field can be used to identify the core and delta in an image

6. PCA applied to fingerprints
We can align different images so that their cores line up
We can then apply PCA to the orientation fields just as we did to face images
We can project a new unknown image into the PC space and find the nearest matches in the training set.

7. Accurate Matching
Orientation fields and PCA are not good enough to give an accurate match
They can reduce the number of possible candidates
We can then apply a more accurate but time-consuming technique to the remaining candidate images

8. Minutiae Matching
Minutiae are fine details of the ridges in the fingerprint image such as
Ridge terminations,
Crossovers
Bifurcations etc.
The pattern of minutiae is unique to each individual person

9. Minutiae Types Different systems define different types of minutiae
The most common are terminations (ridge endings) and bifurcations(forks)

10. Binarisation and Thinning
Every pixel is set to either 0 or 1
Thinning
Lines are thinned to a width of 1 pixel

11. Identifying minutiae
Each black pixel in the image is classified using its “crossing number”.
The crossing number of pixel p is defined as:
cn(p)= Half the sum of the differences between adjacent pixels in the 8-neighbourhood of p

12. Identifying Minutiae
If the crossing number cn is equal to 2 then the pixel is not a minutia but a normal intra-ridge pixel
If the crossing number is not equal to 2 then the pixel is some kind of minutia

14. Removing false-minutiae

15. Minutiae on a real image
The position of the minutiae is marked
The direction of the ridge at each minutia is shown as a short line

16. Matching minutiae between fingerprints
We now have to compare minutiae between two different fingerprints to see if they came from the same person
Remember the two images may have been rotated, translated or distorted with respect to one another
We have to find the combination of rotations, translations and distortions which gives the largest number of matched pairs of minutiae

17. Two different impressions of the same finger

18. Matched pairs of minutiae
Each pair must match position, direction and type

19. Hough Transform
Discretise the range of values for translation, rotation and distortion
Set up an accumulator matrix A in which each element represents a different combination of translation, rotation and distortion
For each possible pair of minutiae calculate the best values of translation, rotation and distortion which makes them match
Increase the corresponding element of A by 1
At the end of the process the element of A with the largest value represents the best combination

20. An alternative to minutiae-matching
FingerCodes
1. Centre image on core
2. Divide image into circular zones
3. Pass each zone through a set of 8 Gabor filters (more about this next week)
4. Compare the results using Euclidean distance