Detection and segmentation of latent fingerprints

Latent fingerprints have been used by law enforcement agencies to identify suspects for a century. However, because of poor image quality and complex background noise, latent fingerprints are routinely identified relying on features manually marked by human experts in practice. A large number of latent fingerprints can not be treated in time due to lacking well trained experts, highlighting the need for “lights out” (fully-automatic) systems. In this paper, we propose a systematic algorithm for latent fingerprint detection, segmentation, and orientation field estimation, without any manual markup. Multiple potential latent fingerprints are detected using a sequential pose estimation algorithm. Then, the full orientation field and confidence map of each detected fingerprint are estimated based on localized dictionaries lookup. Finally, the boundary of each latent fingerprint is delineated by analyzing its confidence map. Experiments on a multi-latent fingerprint database and the challenging NIST SD27 latent fingerprint database show the effectiveness of the proposed algorithm.