We study the impact of minutiae errors in the performance of latent fingerprint identification
systems. We perform several experiments in which we remove ground-truth minutiae from …

Automatic latent fingerprint identification is still challenging for biometric researchers. One
infrequently explored approach for improving the identification rate involves stacking latent …

Latent fingerprints are usually processed with Automated Fingerprint Identification Systems
(AFIS) by law enforcement agencies to narrow down possible suspects from a criminal …

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural
networks with compact feature representation for fast comparison of minutiae sets …

Latent fingerprint matching is a daunting task, primarily due to the poor quality of latent
fingerprints. In this study, we propose a deep-learning based dense minutia descriptor …

Compared to minutia-based fingerprint representations, fixed-length representations are
attractive due to simple and efficient matching. However, fixed-length fingerprint …

For simplicity of pattern recognition system design, a sequential approach consisting of
sensing, feature extraction and classification/matching is conventionally adopted, where …

One of the most challenging problems in fingerprint recognition continues to be establishing
the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene …

Latent fingerprints are one of the most widely used forensic evidence by law enforcement
agencies. However, latent recognition performance is far from the exemplary performance of …

The performance of any fingerprint identification system depends on the accuracy of the
feature extraction stage. Many latent fingerprint matching methods failed to produce good …