Name
Abstract | ||
|---|---|---|
Dense fingerprint registration in the preprocessing stage plays a vital role in the subsequent fingerprint fusion, mosaic, and recognition. However, the existing conventional methods are limited by handcraft features, while the methods based on deep learning lack a large amount of ground truth displacement fields. To overcome these limitations, we propose a self-supervised learning model to directly output densely registered fingerprints. With a spatial transformation network (STN) connected after fully convolutional network (FCN), image deformation interpolation can be achieved to obtain the registered image. Self-supervised training is achieved by maximizing the similarity of images, without the need for ground truth displacement fields. We evaluate the proposed model on publicly available datasets of internal-external fingerprint image pairs. The results demonstrate that the accuracy of the model is comparable to that of the conventional fingerprint registration while executing orders of magnitude faster. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86608-2_31 | BIOMETRIC RECOGNITION (CCBR 2021) |
Keywords | DocType | Volume |
Fingerprint registration, Self-supervised, Convolutional networks | Conference | 12878 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Yu | 1 | 484 | 55.96 |
| Haixia Wang | 2 | 132 | 27.85 |
| Zhang Yilong | 3 | 1 | 2.38 |
| Peng Chen | 4 | 14 | 7.57 |