The relation between sensitivity, similar non-matches and database size in fingerprint database searches

• NCJ Number: 307054
• Journal: Law Probability & Risk Volume: 13 Issue: 2 Dated: 2014 Pages: 151-168
• Author(s): T. Busey; A. Silapiruti; J. Vanderkolk
• Date Published: 2014
• Length: 18 pages
• Grant Number(s): 2009-DN-BX-K226; 2005-MU-BX-K076
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: The information content of friction ridge impressions as revealed by human experts; Adding human expertise to the quantitative analysis of fingerprints

Annotation

The authors address the problem of erroneous identification due to similar but non-matching fingerprints, stating that larger databases tend to increase the likelihood of finding a suspect in the database along with  the number of close non-matching prints; they present solutions to this problem as well as discussing implications for forensic science practitioners.

Abstract

Searching against larger Automated Fingerprint Identification System (AFIS) databases may increase the likelihood of finding a suspect in the database. However, Dror and Mnookin (2010) have argued that this also leads to an increase in the number of similar non-matching prints, which could lead to an erroneous identification. Using simulations, the authors explore the relation between database size and two outcome factors: close non-matching prints and overall database sensitivity, which is a measure of discriminability between true matches and close non-matches. The authors find that larger databases tend to increase both the likelihood of finding the suspect in the database as well as the number of close non-matching prints. However, the former tends to asymptote while the latter increases without bound, and this leads to an initial increase and then a decrease in the sensitivity of the database as more prints are added. This suggests the existence of an optimal database size, and that caution should be observed when interpreting results from larger databases. Quantitative evidentiary techniques such as likelihood ratios have the potential to address some of these concerns, although they too must consider the database size when calculating the likelihood ratio. Implications for practitioners are discussed. (Publisher Abstract Provided)