Volume 55, Issue 2 pp. 341-347

Pilot Study of Automated Bullet Signature Identification Based on Topography Measurements and Correlations* †

Wei Chu Ph.D.

Wei Chu Ph.D.

National Institute of Standards and Technology, Gaithersburg, MD 20899.

Harbin Institute of Technology, Harbin 150001, China.

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John Song M.S.

John Song M.S.

National Institute of Standards and Technology, Gaithersburg, MD 20899.

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Theodore Vorburger Ph.D.

Theodore Vorburger Ph.D.

National Institute of Standards and Technology, Gaithersburg, MD 20899.

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James Yen Ph.D.

James Yen Ph.D.

National Institute of Standards and Technology, Gaithersburg, MD 20899.

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Susan Ballou M.S.

Susan Ballou M.S.

National Institute of Standards and Technology, Gaithersburg, MD 20899.

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Benjamin Bachrach Ph.D.

Benjamin Bachrach Ph.D.

Intelligent Automation Inc., Rockville, MD 20855.

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First published: 01 March 2010
Citations: 28
Additional information and reprint requests:
Wei Chu, Ph.D.
Guest Researcher
National Institute of Standards and Technology
100 Bureau Drive, Stop 8212
Gaithersburg
MD 20899-8212
E-mail: [email protected]
*

Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

Funding provided by the National Institute of Justice (NIJ) through the Office of Law Enforcement Standards (OLES) at NIST.

Abstract

Abstract: A procedure for automated bullet signature identification is described based on topography measurements using confocal microscopy and correlation calculation. Automated search and retrieval systems are widely used for comparison of firearms evidence. In this study, 48 bullets fired from six different barrel manufacturers are classified into different groups based on the width class characteristic for each land engraved area of the bullets. Then the cross-correlation function is applied both for automatic selection of the effective correlation area, and for the extraction of a 2D bullet profile signature. Based on the cross-correlation maximum values, a list of top ranking candidates against a ballistics signature database of bullets fired from the same model firearm is developed. The correlation results show a 9.3% higher accuracy rate compared with a currently used commercial system based on optical reflection. This suggests that correlation results can be improved using the sequence of methods described here.

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