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

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

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

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.

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

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

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

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

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

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

Removing false-minutiae

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

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

Two different impressions of the same finger

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

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

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