Filter Design Based on Spectral Dictionary for Latent Fingerprint Pre-enhancement

Abstract

We introduce a pre-enhancement algorithm to improve efficiency of the automatic fingerprint identification systems (AFIS) for latent fingerprint search. The proposed algorithm employs learning to construct a spectral dictionary from spectral responses of a Gabor filter bank in the frequency domain. Given an input latent fingerprint, the spectral dictionary yields a set of appropriate filters for each partitioning window of the entire latent fingerprint image. The proposed set of spectral filters helps improve and preserve highly-curved ridges in region around the singular point, while the other methods fail. The proposed method outperforms state-of-the-art algorithms in identification accuracy with the good and bad cases of the NIST SD27 latent fingerprint database.