Name
Abstract | ||
|---|---|---|
Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern. To overcome the limitation of handcraft features, we propose to train an end-to-end network to directly output pixel-wise displacement field between two fingerprints. The proposed network includes a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. By applying displacement fields reliably estimated by tracing high quality fingerprint videos to challenging fingerprints, we synthesize a large number of training fingerprint pairs with ground truth displacement fields. In addition, based on the proposed registration algorithm, we propose a fingerprint mosaicking method based on optimal seam selection. Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous dense registration methods in accuracy. Mosaicking experiments on FVC2004 DB1_A and a small fingerprint database demonstrate that the proposed algorithm produced higher quality fingerprints and led to higher matching accuracy, which also validates the performance of our registration algorithm. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TIFS.2020.3017926 | IEEE Transactions on Information Forensics and Security |
Keywords | DocType | Volume |
Fingerprint,registration,deep learning,mosaicking | Journal | 16 |
ISSN | Citations | PageRank |
1556-6013 | 0 | 0.34 |
References | Authors | |
23 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cui Zhe | 1 | 0 | 0.34 |
| Jianjiang Feng | 2 | 814 | 62.59 |
| Jie Zhou | 3 | 2103 | 190.17 |