Date of Graduation

2017

Document Type

Thesis

Degree Type

MS

College

Eberly College of Arts and Sciences

Department

Forensic and Investigative Science

Committee Chair

Keith B Morris

Committee Co-Chair

Afzel Noore

Committee Member

Jacqueline A Speir

Abstract

Latent fingerprints are one of the most common pieces of evidence found on a crime scene and represent accidental or unintentional prints collected as part of a criminal investigation. They are caused when the friction ridge skin comes in contact with a surface, and thus requires the use of chemical processing to be visualized with the naked eye. The comparison and identification of fingerprints depends on various factors such as the substrate quality, surface, duration, environmental factors and examiner experience. These factors can result in reduced clarity or content, and can even cause distortions as compared to a fingerprint taken under controlled conditions. Since the release of the National Academy of Sciences (NAS) report in 2009, the field of fingerprint analysis has come under much scrutiny. Specifically, the need for more research into the determination of the accuracy and reliability of the identifications made by fingerprint examiners has been raised.;One such method used for the comparison of latent fingerprint to known prints is through an Automated Fingerprint Identification System (AFIS). The AFIS used in this research was the AFIX Tracker R where where variables were assessed: match score, match minutiae, match status, delta match score and marked minutiae, to determine which variable(s) was a better indicator of a true match. Bayesian networks were then constructed to compute the likelihood ratios to evaluate the dependency of the variables on one another,where the performance of the likelihood ratios in determining the identity of the unknown latent was assessed using Tippett and ECE plots. Receiver Operating Characteristic (ROC) curves and Bayesian networks were constructed to perform statistical analysis of the matches obtained while comparing a latent print to a ten-print card. A combination of Tippett and Empirical Cross Entropy (ECE) plots were used to assess the performance of the AFIX Tracker R in classifying unknown prints. It was observed that a match minutiae of 15 or higher resulted in a 100% true match result whereas for the non-matches,no more than 13 match minutiae were found. Moreover, the delta match scores difference between the matches and non-matches were notable (delta score of 0.1-153 for matches compared to a score of 0-0.1 for the non-matches). Overall, it was determined that approximately 87% of the time a randomly selected known match would have a higher number of match minutiae as compared to a non-match.

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