Anil K. Jain and Jianjiang Feng
March, 2009
Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. Poor quality of ridge impressions, small finger area and large non-linear distortion are the main difficulties in latent fingerprint matching, compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints to rolled fingerprints that is needed in forensics applications. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map and skeleton. Our system was tested by matching 258 latents in NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining NIST SD4, SD14 and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9% was improved to 74% when extended features are used. In order to evaluate the relative importance of each extended feature, these features are 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 the matching accuracy.
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