Forensic Topography at NIST:

Ballistics and Associated Toolmarks

Richard Silver

John Song1, Johannes Soons1 , Alan Zheng1

Daniel Ott1, Thomas Renegar1, Michael Stocker1, 

Ted Vorburger1, Zheng Chen1, Wei Chu1,

James Yen2, Nien-Fan Zhang2, Robert Thompson3

National Institute of Standards and Technology

1Engineering Physics Division

2Statistical Engineering Division

3Special Programs Office
2

NIST Focus Area: Ballistics and Associated Tool Marks

Goals:

• Metrology infrastructure for objective forensic firearm and tool mark 

identification.

• Scientifically justified protocols that yield objective determinations of 

identification with well-characterized error rates.

Objectives:

• Metrics, procedures, quantitative error rates and uncertainties.

• Metrology, quality assurance, and standards.

• Scientific knowledge base for similarity of marks and tool mark variability. 

Motivation: 2009 NRC Report1

• “..the decision of the tool mark examiner remains a subjective decision 

based on unarticulated standards and no statistical foundation for 

estimation of error rates.”1

1National Research Council, “Strengthening Forensic Science in the United States: A Path Forward,” National Academies Press, 2009
Major Collaborators:

• U.S. Law Enforcement: DoJ/NIJ, ATF, FBI, State and local crime labs

• Universities and forensic institutes: John Jay College, University of 

Central Oklahoma, Iowa State University, UC Davis, RTI International, 

Netherlands Forensics Institute, National Institute for Criminalistics 

and Criminology (BE), California Criminalistics Institute.

• Industry: Cadre Research (Gelsight), Alicona, X-wave Innovations, 

Leeds Forensic Systems, Leica, Intelligent Automation Inc., Sensofar.

• Forensic Organizations: AFTE, IAI, AAFS, CAC, ENFSI, OSAC
Ballistics and Toolmarks: Major Efforts

1. Quality assurance, reference artifacts, documentary standards

2. Metrics and algorithms for objective identification

3. Quantitative uncertainty evaluation

4. Ballistics tool mark database for research and validation

5. Tool mark identification for non-firearm tools

6. Applications to forensic pattern matching

7. Optics research, Fourier methods, E&M modeling

Exclusion Identification

Modeled probability distributions

Data Fit

Target 1, x pol. 4 2 0 2 4

0

20

40

60

80

100

Position m

Intensity a.u.

Data Fit

Target 1, y pol. 4 2 0 2 4

0

50

100

150

200

Position m

Intensity a.u.

z = -2 mm

z = 0 mm

z = 2 mm

z = -1 mm

z = 1 mm
Hardware Platforms: 3-D Topography

3-D optical measurements: Repeatable, more information, variety of techniques.
Standards for Quality Control

• SRM 2460 Standard Bullet

• Machined from pure copper

• 9 mm diameter

• 6 land impressions

• SRM 2461 Standard Casing

• 9 mm cartridge case

• 3 measurement regions 

o firing pin

o breech face

o ejector mark

• SRM 2460a (in development)

• Uses polymer replication process

• Gold coated for durability

• High quality, but economical 

Physical standards for ballistic measurement traceability and quality control. 
1. Performance Evaluation Standards

• Specifications for instrument-to-instrument comparisons 

2. Ballistics-oriented Reference Standards

• Standards in cartridge case format (step height, resolution targets, 

etc.) with dimensions suitable for calibrating 3D instruments

3. Standardized quality assurance methods 

• Provide necessary detail for implementing 3D ballistic imaging 

measurement assurance system

A Metrology Foundation for 3D Ballistic Imaging

(Recent NIJ funded project.)

Cartridge case mounted artifacts
New Algorithms: Congruent Matching Cells (CMC) Method

• Congruent Matching Cross-sections (CMX) for firing pins

• Congruent Matching Profile Segments (CMPS) for Bullets

• Congruent Matching Features (CMF) for complex features

?

J. Song, “Proposed NIST Ballistics Identification System (NBIS) 

using 3D Topography Measurements on Correlation Cells”, AFTE 

Journal, 45 (2), 184-194, 2013.

• Registration position x, y, threshold Tx, Ty

• Registration angle θ, threshold Tθ 

• Correlation value CCFmax, threshold TCCF

If CMC ≥ 6 → Match,

If CMC < 6 → Non-match.

Less bias from areas of 

poor impression
Exclusion Identification

Modeled probability distributions

Error rates, uncertainty procedures, and likelihood ratios

Key challenges:

• Reliable uncertainties require accurate models for tails of the probability distributions 

with limited data and an understanding of dependencies. We are now developing new 

test data sets and population models.

Observed and modeled CMC score distribution for 

780 image comparisons from 10 pistol slides. 

Cumulative false positive error rate.

Likelihood Ratio =

True positive rate

False positive rate

We can express the reliability of the forensic 

test using a likelihood ratio.