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NCJ Number: NCJ 240690   Add to Shopping cart   Find in a Library
Title: Development of Synthetically Generated LEA Signatures to Generalize Probability of False Positive Identification Estimates
Author(s): Benjamin Bachrach ; Pan Gao ; Roger Xu ; Wei Wang ; Ajay Mishra ; Kaizhi Tang ; Guangfan Zhang
Date Published: 01/2013
Page Count: 72
Sponsoring Agency: National Institute of Justice
US Department of Justice
Office of Justice Programs
United States of America
Grant Number: 2009-DN-BX-K236
Sale Source: NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF 
Type: Report (Study/Research)
Language: English
Country: United States of America
Annotation: This project developed a bullet data synthesis to generate Land Engraved Area (LEA) signatures by determining the characteristics of each brand and the details of each barrel.
Abstract: This project was prompted by the nature of automated ballistics identification (ABI) systems, which require a large amount of data in order to evaluate performance. It is impractical to fire such a large number of bullets for this purpose. In order to address this circumstance, the current project developed a bullet data synthesis methodology for a class of guns based on a set of fired sample bullets. The results from the sample bullets can be used to generalize to a much larger population of firearms. The LEA signature is a one-dimensional signal computed from a digitized three-dimensional bullet surface. The LEA signature is used for bullet matching in ABI systems. The approach used in this project consists of a deterministic component and a random component. The deterministic component includes periodicities in a base curve profile, and the random component is best represented by the fractal model of an irregular curve. The parameters used in the generation of the base and fractal curves are obtained from signal analysis based on wavelet transformation. The details of this method are described in this report. An optimization procedure was applied to the synthesis process in order to ensure the matched and non-matched correlation distribution of the new synthesized dataset resemble that of the existing data of the same brand. Researchers also extended the data synthesis process from a one-dimensional LEA signature signal to a two-dimensional LEA image and further to a three-dimensional bullet surface. According to preliminary data synthesis results, newly generated LEAs for a brand show strong similarity to the LEAs in the same brand, but no similarity to different brands. 53 figures, 8 tables, and 20 references
Main Term(s): Forensics/Forensic Sciences
Index Term(s): Ballistics ; Firearms identification ; Automation ; Investigative techniques ; Firearm tracing ; NIJ final report ; NIJ grant-related documents
   
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https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=262770

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