To compensate for the different orientations of two fingerprint images, matching systems use a reference point and a set
of transformation parameters. Fingerprint minutiae are compared on their positions relative to the reference points, using
a set of thresholds for the various matching features. However a pair of minutiae might have similar values for some of
the features compensated by dissimilar values for others; this tradeoff cannot be modeled by arbitrary thresholds, and
might lead to a number of false matches. Instead given a list of potential correspondences of minutiae points, we could
use a static classifier, such as a support vector machine (SVM) to eliminate some of the false matches. A 2-class model is
built using sets of minutiae correspondences from fingerprint pairs known to belong to the same and different users. For
a test pair of fingerprints, a similar set of minutiae correspondences is extracted and given to the recognizer, using only
those classified as genuine matches to calculate the similarity score, and thus, the matching result. We have built
recognizers using different combinations of fingerprint features and have tested them against the FVC 2002 database.
Using this recognizer reduces the number of false minutiae matches by 19%, while only 5% of the minutiae pairs
corresponding to fingerprints of the same user are rejected. We study the effect of such a reduction on the final error rate,
using different scoring schemes.