Regarding the first issue, the study found automatic systems have not fully used the knowledge gained by forensic experts in manual fingerprint matching regarding extended fingerprint features, namely, ridge skeletons, pores, incipients, and dots. The current study proposes methods to automatically extract and compare these extended features in a hierarchical fashion. Using these methods, experiments showed improvement in both live-scan and latent matching after extended features were incorporated. In developing the proposed methods, researchers conducted statistical analysis on the reproducibility and individuality of the extended features of fingerprints and demonstrated their distinctive nature both theoretically and empirically. In addressing the second issue, researchers investigated the interoperability between a new fingerprint sensing technology based on touchless imaging and the legacy rolled-fingerprint data. This led to the development of a nonparametric virtual rolling method that unwraps the three-dimensional touchless fingerprints into two-dimensional rolled-equivalent fingerprints. Researchers also developed a quality measure and an enhancement algorithm for touchless fingerprints. Experiments on a small database with 102 fingers showed the effectiveness of the proposed methods in achieving compatibility in matching touchless fingerprints with legacy rolled fingerprints. This report also proposes future research on extended fingerprint features and sensor interoperability. Extensive figures, tables, and mathematical formulas, and a 136-item bibliography
Extended Feature Set and Touchless Imaging for Fingerprint Matching
NCJ Number
227931
Date Published
June 2009
Length
209 pages
Annotation
This study addressed two critical issues related to the transition from manual fingerprint matching to the use of biometric technology: the failure of automatic systems to make full use of the knowledge gained from manual fingerprint matching and the failure of automatic systems to fully achieve interoperability between advanced sensing technology and legacy fingerprint databases.
Abstract
Date Published: June 1, 2009