  Latent fingerprint matching is a very important but unsolved problem. As a
key step of fingerprint matching, fingerprint registration has a great impact
on the recognition performance. Existing latent fingerprint registration
approaches are mainly based on establishing correspondences between minutiae,
and hence will certainly fail when there are no sufficient number of extracted
minutiae due to small fingerprint area or poor image quality. Minutiae
extraction has become the bottleneck of latent fingerprint registration. In
this paper, we propose a non-minutia latent fingerprint registration method
which estimates the spatial transformation between a pair of fingerprints
through a dense fingerprint patch alignment and matching procedure. Given a
pair of fingerprints to match, we bypass the minutiae extraction step and take
uniformly sampled points as key points. Then the proposed patch alignment and
matching algorithm compares all pairs of sampling points and produces their
similarities along with alignment parameters. Finally, a set of consistent
correspondences are found by spectral clustering. Extensive experiments on
NIST27 database and MOLF database show that the proposed method achieves the
state-of-the-art registration performance, especially under challenging
conditions.
