  Skin distortion is a long standing challenge in fingerprint matching, which
causes false non-matches. Previous studies have shown that the recognition rate
can be improved by estimating the distortion field from a distorted fingerprint
and then rectifying it into a normal fingerprint. However, existing
rectification methods are based on principal component representation of
distortion fields, which is not accurate and are very sensitive to finger pose.
In this paper, we propose a rectification method where a self-reference based
network is utilized to directly estimate the dense distortion field of
distorted fingerprint instead of its low dimensional representation. This
method can output accurate distortion fields of distorted fingerprints with
various finger poses and distortion patterns. We conducted experiments on
FVC2004 DB1\_A, expanded Tsinghua Distorted Fingerprint database (with
additional distorted fingerprints in diverse finger poses and distortion
patterns) and a latent fingerprint database. Experimental results demonstrate
that our proposed method achieves the state-of-the-art rectification
performance in terms of distortion field estimation and rectified fingerprint
matching.
