  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 and
efficiency. Mosaicking experiment on FVC2004 DB1 demonstrates that the proposed
algorithm produced higher quality fingerprints than other algorithms which also
validates the performance of our registration algorithm.
