  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. Considering the limited number and variety of distorted
fingerprints in the existing public dataset, we collected more distorted
fingerprints with diverse finger poses and distortion patterns as a new
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.
