Wei Chu, Robert Thompson, John Song, and 

Theodore Vorburger

Presented by Robert M. Thompson

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Disclaimer

Certain commercial equipment, instruments, or 

materials are identified in this report in order to specify 

the experimental procedure adequately. Such 

identification is not intended to imply recommendation 

or endorsement by the National Institute of Standards 

and Technology, nor is it intended to imply that the 

materials or equipment identified are necessarily the 

best available for the purpose.

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Traditional examination method: Comparison microscopy

Striation marks are produced by topographical features

inside the barrel.

Ballistic Identification
Some early models for statistically valid 

and objective criterion for identification 

of toolmark stria

Consecutive Matching Stria; Biasotti; 1959

 Surface featured to “Bar Code”; Blackwell & 

Framan; 1980

Mathematical Models using rare features; 

Uchiyama; 1980

Model for Automated Identification; 

Uchiyama; 1988

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National Research Council, NAS Reports

“Ballistic Imaging” 2008

“The validity of the fundamental assumptions of 

uniqueness and reproducibility of firearms-related tool 

marks has not yet been fully demonstrated.”

“Strengthening Forensic Science in the United States: 

A Path Forward” 2009

“The fact is that many forensic tests—such as those used 

to infer the source of tool marks or bite marks—have 

never been exposed to stringent scientific scrutiny.”

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NIST 3D measurement system

Specifications

for 20x and 50x 

lens:

• Measurement 

field: 800/320 

μm 

• X/Y-Resolution: 

1.5/0.6 μm.

• Z-Resolution: 

20/10 nm.

Nanofocus Nipkow disk confocal microscope

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Evaluation of the similarity

(Wisconsin Department of Justice, http://www.doj.state.wi.us/dles/crimelabs/Firearms.asp)

 



  

  



k

i i

k

i i

k

i

r k r l k l

r k r l k l

r l

2 2

( ( ) ) ( ( ) )

(( ( ) )*( ( ) ))

CCF( , , )







A=B A≠B

Metric: 

Cross-correlation function (CCF)

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Application of CCF in NIST standard bullet 

comparison; mathematical comparison of replicas

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NIST SRM 2460 Standard Bullets

An example of CCF calculation result

Golden Images for SRM Bullets 

Land 1-3

Land 4-6
Application example for a fired bullet

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Bullet signature

Data acquisition 

Processing

correlation
Selection of valid correlation areas

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Bullets land impression images may include 

areas contain useless or wrong striation 

information

Question: Can valid striated areas be intelligently 

distinguished from other invalid areas? 
How do we improve on selection? 

─ Application of edge detection technology

• Definition:

Edge detection is a process that mainly measures, detects, 

and localizes changes of intensity. 

• Property:

An edge as described here does not necessarily 

correspond to an object boundary, but edges have the 

desirable property of drastically reducing the amount of 

information to be processed subsequently while preserving 

information about the shapes of objects in a scene. 

• Edge Detection Methods:

Roberts, Prewitt, Sobel, Log, Canny, Wavelet, 

Morphology...

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Example of applying Canny edge detector

• Canny detector - An optimal edge detection algorithm

Properties: good detection, good location, minimum response

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Ref: 

J Canny. A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and 

Machine Intelligence, 8: 679-714, 1986.
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Canny edge detection result

Topography Image 

after preprocessing

Apply Canny edge detector on bullet land 

impression image 

Striation edge image
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Applications of edge detection results (1)

─ to predict the identifiability of fired bullets

Striation density(Beretta): 5.83%

Striation density(Taurus ): 2.27% 

Striation density(Bryco ): 0.16%

NIST standard bullet: 7.49%
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a

b

c d

e

f

h

a. Flattened image after 

confocal image 

preprocessing

b. Striation edge image

c. Mask image

d. Image with invalid area 

removed

e. Upright image after 

rotation

f. Signature profile 

Steps to remove invalid area for correlation 

Applications of edge detection results (2)

─ removal of invalid correlation area
Experiment

Reference:

J Hamby, D Brundage, The identification of bullets fired from 10 consecutively rifled 9mm Ruger pistol 

barrels: a research project involving 507 participants from 20 countries, AFTE Journal, 41(2):99-110

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Sample: 20 known matching + 15 unknown bullets 

fired from 10 consecutively rifled barrels, each 

bullet with 6 LEA’s

Participants: We are 551st, as of August, 2010

Comparison methods: 

1. Cross correlation function (CCF)

2. Consecutive matching stria (CMS)
CCF results

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LEA comparison Bullet comparison

Correlation values of all ten pairs of known-matching bullets scored

highest on their correlation lists, yielding a correct identification rate

100 %. 

For 15 unknown bullets, all 30 pairs of matching bullets scored at 

the topmost position on their respective correlation lists. (Blind Test)

See case 

analysis 
Case analysis

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Confocal raw images; LEA to LEA comparison 

Processing

There is only one comparison of a matching LEA that did 

not have matching correlation score (22.85 %)
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Definition

In three-dimensional tool marks when at least two different 

groups of at least three consecutive matching stria appear in 

the same relative position, or one group of six consecutive 

matching stria are in agreement in an evidence tool mark 

compared to a test tool mark

CMS conservative numerical criteria:

Originally, the CMS criteria is employed in comparisons of 

optical images using comparison microscopy. Can it be 

extended to 3D topography data? Can we “unify” the two 

methods?
Model based CMS counting

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Since the areas that do not contain valid stria marks are 

identified and removed for subsequent processing, the 

generated feature profiles will be highly consistent with 

stria marks.
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CMS comparison results of known matching bullets 

CMS results for known non-matching LEA comparison (of total 12960)

CMS results for known matching LEA comparisons (of tally 60)
Results

10 pairs of known-matching bullets – all 

correctly identified

15 unknown bullets – each has 2 

counterparts in known-matching group 

bullets.

29 of 30 bullet comparisons are 

correctly identified.

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Significance Outcomes

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• Filled the gap between manual

operation and automated systems.

• Increased the objectivity of firearm

identification processing.

• Has produced solid groundwork

for future statistical analysis.
Questions?

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