Fingerprint Matching with Localized Deep Representation
Yongjie Duan Zhiyu Pan Jianjiang Feng Jie Zhou
Compared to minutia-based fingerprint representations, fixed-length
representations are attractive due to simple and efficient matching. However,
fixed-length fingerprint representations are limited in accuracy when matching
fingerprints with different visible areas, which can occur due to different
finger poses or acquisition methods. To address this issue, we propose a
localized deep representation of fingerprint, named LDRF. By focusing on the
discriminative characteristics within local regions, LDRF provides a more
robust and accurate fixed-length representation for fingerprints with variable
visible areas. LDRF can be adapted to retain information within any valid area,
making it highly flexible. The matching scores produced by LDRF also exhibit
intuitive statistical characteristics, which led us to propose a matching score
normalization technique to mitigate the uncertainty in the cases of very small
overlapping area. With this new technique, we can maintain a high level of
accuracy and reliability in our fingerprint matching, even as the size of the
database grows rapidly. Our experimental results on 21 datasets containing over
140K fingerprints of various finger poses and impression types show that LDRF
outperforms other fixed-length representations and is robust to sensing
technologies and impression types. Besides, the proposed matching score
normalization effectively reduces the false match rate (FMR) in large-scale
identification experiments comprising over 5.11 million fingerprints.
Specifically, this technique results in a reduction of two orders of magnitude
compared to matching without matching score normalization and five orders of
magnitude compared to prior works.