In this paper we introduce a novel method of fingerprint alignment that uses the intrinsic geometric properties of
minutiae-based triangles combined with the geometric invariant. The minutiae points are extracted from the
fingerprint image and a Delaunay (DL) triangulation is constructed from these minutiae points resulting in a
series of triangles. Corresponding minutiae points are established using local affine invariants constructed from
the local minutia-based triangles. Triangles that are distorted by noise or have no counter part on the query are
discarded. We rely only on “strong” matches that are reliable and present, for example, where the error metric
between the local absolute invariants is below a set threshold. The correspondences of such matches are then
used to estimate transformation parameters. The performance of our method is represented by computing the
distance map error between a template and a query fingerprint after undoing the transformation, computed from
the ridge structures of the two fingerprints. In conclusion, the proposed method can be used to find the
corresponding minutiae and align any fingerprints considered into affine transformation, in the presence of noise
including the partial occlusion
