  Minutiae extraction is of critical importance in automated fingerprint
recognition. Previous works on rolled/slap fingerprints failed on latent
fingerprints due to noisy ridge patterns and complex background noises. In this
paper, we propose a new way to design deep convolutional network combining
domain knowledge and the representation ability of deep learning. In terms of
orientation estimation, segmentation, enhancement and minutiae extraction,
several typical traditional methods performed well on rolled/slap fingerprints
are transformed into convolutional manners and integrated as an unified plain
network. We demonstrate that this pipeline is equivalent to a shallow network
with fixed weights. The network is then expanded to enhance its representation
ability and the weights are released to learn complex background variance from
data, while preserving end-to-end differentiability. Experimental results on
NIST SD27 latent database and FVC 2004 slap database demonstrate that the
proposed algorithm outperforms the state-of-the-art minutiae extraction
algorithms. Code is made publicly available at:
https://github.com/felixTY/FingerNet.
