FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction
This work addresses a critical bottleneck in automated fingerprint recognition for latent fingerprints, offering a domain-specific improvement.
The authors tackled the problem of minutiae extraction in latent fingerprints, which previous methods failed on due to noise, and proposed FingerNet, a unified deep network that outperformed state-of-the-art algorithms on NIST SD27 and FVC 2004 databases.
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.