A utility of pores as level 3 features in latent fingerprint identification

Latent fingerprint identification is the most prevalent process used by the forensic community from a long time. Smudgy, blurred, and small fingerprint area of the latent impressions results into the deficiency of the level 2 features i.e. minutiae. Most of the commercially available Automated Fingerprint Identification Systems (AFISs) which mainly dependent on minutiae; seems less efficient in latent fingerprint matching. In this paper, the usefulness of pores (level 3 features) besides minutiae in latent fingerprint matching is examined. An algorithm based on Lindeberg's automatic scale selection method is proposed for pores extraction in latent fingerprints. The fusion of pores and minutiae at score level is used to re-rank the minutiae based latent matcher. The effectiveness of the proposed algorithm and pores utility are evaluated by observing and comparing the latent recognition accuracy obtained for minutiae matching and matching after fusion. Both minutiae and pores are automatically extracted in latent and reference fingerprints. The experimental results show that the fusion significantly improves the latent recognition rate in comparison to the minutiae matching.

Automated summary

Latent fingerprint identification, the most prevalent process used by the forensic community, is improved by proposing an algorithm that utilizes pores in addition to minutiae for matching. Experimental results demonstrate that the fusion of pores and minutiae significantly enhances the latent fingerprint recognition rate.