This article proposes a system for matching latent fingerprints found at crime scenes to rolled fingerprints in law enforcement databases.
Latent fingerprint identification is critically important to law enforcement agencies in identifying suspects. Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. Although tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. In addition to minutiae, the proposed system for fingerprint matching also uses extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. The authors tested their system by matching 258 latent prints in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving matching accuracy. (publisher abstract modified)