Latent Fingerprint Matching : Utility of Level 3 Features

Automatic fingerprint identification systems (AFIS) have for a long time used only minutiae for fingerprint matching. But minutiae are only a small subset of fingerprint detail routinely used by latent examiners for fingerprint matching. This has generated a lot of interest in extended feature set (EFS) with the aim of narrowing down the gap between the performance of AFIS and latent examiners. Level 3 features constitute the most significant subset of extended features. Studies on level 3 features have reported significant improvement in the fingerprint recognition accuracy. However, these studies were based either on live-scan fingerprints or full (rolled or slap) fingerprints. As a result, the conclusions of these studies cannot be extended to latent fingerprints, which are characterized by small size, poor image quality, and severe distortion compared to full fingerprints. In this paper, we study the utility of level 3 features, including pores, dots, incipient ridges, and ridge edge protrusions, for latent matching. Automatic algorithms for extracting and matching these features are proposed. While most existing level 3 feature matching algorithms only consider the locations of features, the proposed method utilizes the topological relationship between level 3 and level 2 features, and is thus robust to nonlinear distortion and has high discriminative capability. Given the proposed algorithms and operational latent fingerprint databases, we identify the challenges in using level 3 features, and show the potential of level 3 features in improving latent matching accuracy. Further, by using simulated partial fingerprints, we highlight that level 3 features can indeed improve latent matching accuracy when i) level 3 features can be reliably extracted in both latent and full fingerprints and ii) latent fingerprints have only a small number of minutiae or the minutiae match scores are low. With the increasing adoption of 1000ppi fingerprint scanners in law enforcement agencies, it is becoming feasible and desirable to incorporate level 3 features into AFIS. We believe that the proposed algorithms and analysis will be useful in the design and development of next generation AFIS.