CCI Firearms and Toolmark Examiner Academy

CCI Firearms and Toolmark Examiner Academy Workshop on Current Firearms and Toolmark

Research

Pushing Out the Frontiers of Forensic Science

3 2 1 0 1 2 3

Outline

• Morning-ish

• Introduction and the Daubert Standard

• Confocal Microscopy

• Focus Variation Microscopy

• Interferometric Microscopy

• Surface Data/Filtering

Outline• Afternoon-ish

• Similarity scores and Cross-correlation functions

• Known Match/Known Non-Match Similarity Score histograms. False Positives/False Negatives/Error Rates

• Multivariate Discrimination of Toolmarks• Measures of “Match Quality”

• Confidence• Posterior Error Rate/Random Match

Probability• Lessons learned in conducting a successful

research project

Introduction• DNA profiling the most successful application of

statistics in forensic science.• Responsible for current interest in “raising standards” of

other branches in forensics…??

• No protocols for the application of statistics to comparison of tool marks.• Our goal: application of objective, numerical

computational pattern comparison to tool marks

Caution: Statistics is not a panacea!!!!

• Daubert (1993)- Judges are the “gatekeepers” of scientific evidence.

• Must determine if the science is reliable • Has empirical testing been done?

• Falsifiability

• Has the science been subject to peer review?

• Are there known error rates?

• Is there general acceptance?

• Federal Government and 26(-ish) States are “Daubert States”

The Daubert Standard

Tool Mark Comparison Microscope

G. Petillo

G. Petillo

4 mm

Known Match Comparisons5/8” Consecutively manufactured chisels

G. Petillo

Known NON Match Comparisons5/8” Consecutively manufactured chisels

G. Petillo

4 mm 4 mm

600 um

5/8” Consecutively manufactured chisels

Marvin Minsky First confocal microscope

Confocal Microscope

Confocal Microscopes

In focus light

Out of focus light

Tool mark surface(profile of a striation pattern)

Focal planefor objective

Sample stage

Objective lens

Illumination aperture

Source

Confocal pinholeDetector

Rastering pattern oflaser confocal

Nipkow disk sweepsmany pinholes

Programmable array Illumination/DetectionGet any illumination/detection pattern

Sample stageScan stage in“z”-direction

Objective’s focal plane


Detector


Sample stage

Scan stage in“z”-direction

Detector


Detector For Each Detector Pixel:

Record the “axial response” as stage is moved along the z-direction

Point on surface corresponding topixel’s is in maximum focus here

Increasing surface height

All-in-Focus 2D Image

Overlay confocal “z-stack”

• 3D confocal image of portion of chisel striation pattern

• Use high NA objectives for best results

• Small working distances

• Flanks up to ~ 70o

• Cost ~150K – 250K (FTI IBIS ~1M)

• Get a vibration isolation table for your instrument ~7K

• Set up in a (dry) basement if possible

• Accuracy down to +/- 10 nm

Confocal Microscope Trivia

Optical slice thickness =

• Some manufactures:

• Olympus

• LEXT (Laser)

• Zeiss

• CSM (White Light)

• LSM (Laser)

• Nanofocus

• msurf series (White Light)

• Sensofar/Leica

• Plu series/DCM (White Light)

Confocal Microscope Trivia

Focus Variation Microscope

Scherer and Prantl

“Low res” commonFocus variation mic

~ +/- 1mm

In focus light

Out of focus light



Sample stage

Objective lens

Source

Detector

Cutaway

Alicona, GMBH

Detector For Each Detector Pixel:

Record the “axial response” as stage is moved along the z-direction

Point on surface corresponding topixel is in maximum focus here

Focus Determination:Detector

Pixel of interest

Compute standard deviation (sd) of pixels grey valuesin the neighborhood

A pixel in focus sits in a neighborhood with a large sd


• Can use external light

• Large working distances

• Flanks up to ~75o

• Cost ~200K – 250K.

• 80K models WON’T have the vertical resolution needed for forensic work



• Accuracy down to +/- 10nm

Focus Variation Microscope Trivia

• Some manufactures:• Alicona

• IFM• Can get optional rotational stage

• Sensofar/Leica

• S neox/DCM

Focus Variation Microscope Trivia

Interferometer

Incoming wave

split

Path lengths equalRecombine in-phase

Fixed mirror

Movable mirrorrecombine

Interferometer

Incoming wave

split

Path lengths NOT equalRecombine out-of-phase

Fixed mirror

Movable mirrorrecombine

Interferometric Height Measurement

• The basic idea:• Each surface point is a “fixed mirror”

• Move a reference mirror in objective

• Split beams recombine in and out of phase

• Constructive interference occurs when surface points in focal plane

• Infer the surface heights from where constructive interference occurs

Interferometric Microscope

James WyantEarly Interferometric

Microscope

Early InterferometricMicroscope for Surafce Metrology

Wyant

Wyant

Modern InterferometricMicroscope for Surafce Metrology



Sample stage

Objective lens

Camera (Detector)Source

MicroscopeConfiguration

PiezoReference mirror

Beam-splitter

Scan objective forInterference in “z”-direction

Path lengths equalPoint in focus


Sample stage

Objective lens

Camera (Detector)Source

MicroscopeConfiguration

PiezoReference mirror

Beam-splitter


Path lengths un-equalPoint in out of focus


Interference Objectives

Mirau objective~ 10X – 100X

Michelson objective~ 2X – 10X

Linnik objective+ 100X

Detector

For Each Detector Pixel:

Record each pixels interference pattern as objective is scanned

Point on surface corresponding To pixel’s is in maximum focus here

Inference patterns:

Sample stage


Fringes

Bruker NSD Bruker NSD

Fringe Pattern Surface

Turn Fringes Into A Surface

Intensity for each detector pixel:

Fourier transform I(z) to get q(k)

Compute surface heights

deGroot

k

arg[

q(k)

]

k0

q

A

with:

Interferometry Trivia


• Small working distances

• Flanks up to ~25o

• Cost ~200K – 250K.



• Comes in two modes

• VSI: Accuracy +/- 10nm

• PSI: Accuracy below 1nm

• Some manufactures:• Bruker (Acquired WYKO/Veeco)

• Taylor Hobson

• Sensofar/Leica

• S neox/DCM

Interferometry Trivia

Surface Data

37.88 37.89 37.89 37.90 37.92 37.91 37.93 37.93 37.94 37.9937.88 37.89 37.87 37.87 37.87 37.85 37.89 37.92 37.97 38.0237.86 37.85 37.84 37.84 37.84 37.85 37.85 37.92 37.98 38.0337.84 37.82 37.81 37.81 37.83 37.85 37.88 37.92 37.97 38.0437.81 37.80 37.80 37.82 37.84 37.86 37.89 37.94 37.98 38.0537.81 37.78 37.79 37.82 37.85 37.89 37.94 37.96 38.00 38.0437.82 37.80 37.80 37.83 37.87 37.91 37.98 37.99 38.02 38.0537.84 37.81 37.80 37.81 37.84 37.89 37.95 37.99 38.01 38.0637.84 37.80 37.76 37.77 37.78 37.86 37.92 37.96 38.00 38.0337.80 37.77 37.76 37.74 37.79 37.84 37.90 37.93 37.98 38.00

Surface heights (mm)

Land Engraved Area:

Point are “double precision”: 64-bits/point BIG FILES!

Surface Data

Detector levels (16-bit values):

Land Engraved Area:

16617 16622 16622 16625 16632 16629 16638 16639 16645 1666516618 16620 16613 16613 16610 16605 16622 16632 16656 1667616606 16602 16600 16597 16597 16603 16604 16632 16662 1668416600 16589 16587 16587 16594 16603 16616 16632 16658 1668616585 16583 16583 16588 16599 16608 16619 16643 16662 1668916587 16572 16579 16590 16604 16622 16641 16652 16669 1668816591 16581 16583 16594 16610 16630 16661 16663 16679 1669216597 16586 16583 16585 16597 16623 16646 16666 16674 1669516599 16581 16566 16569 16574 16607 16634 16651 16669 1668316581 16567 16562 16556 16575 16597 16625 16640 16660 16671

Point are detector grey levels: 16-bits/point Smaller files. Convert to mm in RAM

• Different systems use different storage formats

• Be aware if writing custom apps. ASK COMPANY FOR FILE FORMAT!

• Alicona: Saves surface data as doubles. HUGE FILES!

• Zeiss: Saves surface data as 16-bit grey levels with conversion factor

• Other?? 24, 32-bit detectors now??

• Need to standardize file format!

• X3DZhang,Brubaker

• Digital-Surf .surPetraco

Surface Data Trivia

• Think of a toolmark surface as being made up of a series of waves

Surface Filtering

• Examine different scales by “blocking out” (filtering) some of the sinusoids

Surface Filtering

“Low Pass” filter blocks high frequencies and passes low frequencies (long wavelengths)

• Examine different scales by “blocking out” (filtering) some of the sinusoids

Surface Filtering

“High Pass” filter blocks low frequencies and passes high frequencies (short wavelengths)

• Wavelength “cutoffs”

Surface Filtering Trivia

A “High Pass” filterA “Low Pass” filter

lcut lcut

• Wavelength ranges

• Short wavelengths passed: roughness

• Medium wavelengths passed: waviness

• Long wavelengths passed: form

• Band-pass filter: Select narrow wavelength bands to keep.

• High-pass/Low-pass combinations (Filter banks)

• Wavelets are great at doing this

Surface Filtering

Statistics

Weapon Mark Association

– What measurement techniques can be used to obtain data for toolmarks?

– What statistical methods should be used?• How do we measure a degree of confidence for an association, i.e. a

“match”?• What are the identification error rates for different methods of

identification?

• R is not a black box!• Codes available for review; totally transparent!

• R maintained by a professional group of statisticians, and computational scientists• From very simple to state-of-the-art procedures

available

• Very good graphics for exhibits and papers

• R is extensible (it is a full scripting language)• Coding/syntax similar to MATLAB

• Easy to link to C/C++ routines

Why ?

• Where to get information on R :• R: http://www.r-project.org/

• Just need the base

• RStudio: http://rstudio.org/

• A great IDE for R

• Work on all platforms

• Sometimes slows down performance…

• CRAN: http://cran.r-project.org/

• Library repository for R

• Click on Search on the left of the website to search for package/info on packages

Why ?

http://www.r-project.org/

http://rstudio.org/

http://cran.r-project.org/

Finding our way around R/RStudio

• Gauge similarity between tool marks with one number• Similarity “metric” is a function which

measures “sameness”• Only requirement: s(A,B) = s(B,A)

• There are an INFINITE number of ways to measure similarity!!

Common Computational Practice

• Often max CCF is used.

Cross-correlation

KNM can sometimes have high max-ccf…

max-ccf: 0.751

• Glock primer shear: Each profile ~2+ mm

• Lag over 2000 units (~0.8 mm)• Max CCF distributions

Cross-Correlation

Scores from“Known Non-Matches”

Scores from “Known Matches”

We thought: Ehhhhhh…….

• Random variables - All measurements have an associated “randomness” component

• Randomness –patternless, unstructured, typical, total ignoranceChaitin, Claude

Multivariate Feature Vectors

• For an experiment/observation, put many measurements together into a list • Collection random variables into a list called a

random vector

1. Also called: observation vectors

feature vectors

• Potential feature vectors for surface metrology• Entire surfaces

• *Surface profiles

• Surface/profile parameters

• Surface/profile Fourier transform or wavelet coefficients

• Translation/rotation/scale invariant surface (image) moments

Multivariate Feature Vectors

Mean total profile:

Mean waviness profile:

Waviness profile

Barcode representation

Tool

mar

ks (

scre

wdr

iver

str

iati

on p

rofi

les)

for

m d

atab

ase

Biasotti-Murdock Dictionary

Consecutive Matching Striae (CMS)-Space

Some Important Terms

• Latent Variable: weighted combination of experimental variables into a new “synthetic” variable• Also called: scores, components or factors

• The weights are called loadings

• Most latent variables we will study are linear combinations between experimental variables and loadings:• Dot prod. between obs. vect. and loading vect.

gives a score:

• PCA:

• Is a rotation of reference frame

• Gives new PC directions’ relative importance

• PC variance

Principal Component Analysis

• Technically, PCA is an eigenvalue-problem• Diagonalize some version of S or R to get a PCs

• Typically

Principal Component Analysis

covariancematrix matrix of PC

“loadings”matrix of PC variances

• For a data frame of p variables, there are p possible PCs.

• s ≅ PC importance, dimension reduction

• Scores are data projected into space of PCs retained

• Scores plots, either 2D or 3D

• Need a data matrix to do machine learning

Setup for Multivariate Analysis

Represent as a vector of values

{-4.62, -4.60, -4.58, ...} • Each profile or surface is a row in the data matrix • Typical length is ~4000 points/profile• 2D surfaces are far longer

• HIGHLY REDUNDANT representation of surface data

• PCA can:• Remove much of the redundancy• Make discrimination computations

far more tractable

• How many PCs should we use to represent the data??

• No unique answer

• FIRST we need an algorithm to I.D. a toolmark to a tool

• ~45% variance retained

• 3D PCA of 1740 real and simulated mean profiles of striation patterns from 58 screwdrivers:

Support Vector Machines• Support Vector Machines (SVM) determine

efficient association rules• In the absence of specific knowledge of probability

densities

SVM decision boundary

Support Vector Machines• SVM computed as optimization of “Lagrange

multipliers”

• Quadratic optimization problem • Convex => SVMs unique unlike NNs

• k(xi,xj) kernel function

• “Warps” data space and helps to find separations

• Many forms depending on application: linear, rbf usually

• C: penalty parameter • control the margin of error between groups that are not

perfectly separable: 0.1 to 10 usually

Support Vector Machines

• The SVM decision rule is given as:

• Equation for a plane in “kernel space”

• Multi group classification handled by “voting”

• How many Principal Components should we use?

PCA-SVM

With 7 PCs, expect ~3% error rate

With 13 PCs, expect ~1% error rate

• This supervised technique is called Linear Discriminant Analysis (LDA) in R• Also called Fisher linear discriminant analysis

• CVA is closely related to linear Bayes-Gaussian discriminant analysis

Canonical Variate Analysis

• Works on a principle similar to PCA: Look for “interesting directions in data space”• CVA: Find directions in space which best separate

groups.• Technically: find directions which maximize ratio of

between group to within variation


Project on PC1:Not necessarily good group separation!

Project on CV1:Good group separation!

Note: There are #groups -1 or p CVswhich ever is smaller

• Use between-group to within-group covariance matrix, W-1B to find directions of best group separation (CVA loadings, Acv):


• CVA can be used for dimension reduction.• Caution! These “dimensions” are not at right

angles (i.e. not orthogonal)

• CVA plots can thus be distorted from reality

• Always check loading angles!

• Caution! CVA will not work well with very correlated data

• Distance metric used in CVA to assign group i.d. of an unknown data point:

• If data is Gaussian and group covariance structures are the same then CVA classification is the same as Bayes-Gaussian classification.


• 2D/3D-CVA scores plots of RB screwdrivers

2D CVA 3D CVA


• 2D scores plots of RB screwdrivers:

PCA vs. CVA

2D PCA of striation pattern mean profiles 2D CVA of striation pattern mean profiles

• Discriminant functions are trained on a finite set of data • How much fitting should we do?

• What should the model’s dimension be?

Error Rate Estimation

• Model must be used to identify a piece of evidence (data) it was not trained with. • Accurate estimates for error rates of decision

model are critical in forensic science applications.

• The simplest is apparent error rate:• Error rate on training set

• Lousy estimate, but better than nothing

• Cross-Validation: hold-out chunks of data set for testing • Known since 1940s

• Most common: Hold-one-out

Error Rate Estimation

• Bootstrap: Randomly selection of observed data (with replacement) • Known since the 1970s

• Can yield confidence intervals around error rate estimate

• The Best: Small training set, BIG test set

Refined bootstrapped I.D. error rate for primer shear striation patterns= 0.35% 95% C.I. = [0%, 0.83%]

(sample size = 720 real and simulated profiles)

18D PCA-SVM Primer Shear I.D. Model, 2000 Bootstrap Resamples

How good of a “match” is it?Conformal PredictionVovk

• Data should be IID but that’s it C

umul

ativ

e #

of E

rror

s

Sequence of Unk Obs Vects

80% confidence20% errorSlope = 0.2



• Can give a judge or jury an easy to understand measure of reliability of classification result

• This is an orthodox “frequentist”

approach• Roots in Algorithmic Information

Theory

• Confidence on a scale of 0%-100%

• Testable claim: Long run I.D. error-rate should be the chosen significance level

How Conformal Prediction works for us• Given a “bag” of obs with known identities and one obs of

unknown identityVovk

• Estimate how “wrong” labelings are for each observation with a non-conformity score (“wrong-iness”)

• Looking at the “wrong-iness” of known observations in the bag:

• Does labeling-i for the unknown have an unusual amount of “wrong-iness”??:

• For us, one-vs-one SVMs:

• If not:

• ppossible-IDi ≥ chosen level of significance

• Put IDi in the (1 - )*100% confidence interval

Conformal Prediction

Theoretical (Long Run) Error Rate: 5%

Empirical Error Rate: 5.3%

14D PCA-SVM Decision Modelfor screwdriver striation patterns

• For 95%-CPT (PCA-SVM) confidence intervals will not contain the correct I.D. 5% of the time in the long run

• Straight-forward validation/explanation picture for court

Conformal Prediction Drawbacks

• CPT is an interval method• Can (and does) produce multi-label I.D. intervals• A “correct” I.D. is an interval with all labels

• Doesn’t happen often in practice…

• Empty intervals count as “errors”• Well…, what if the “correct” answer isn’t in the database

• An “Open-set” problem which Champod, Gantz and Saunders have pointed out

• Must be run in “on-line” mode for LRG

• After 500+ I.D. attempts run in “off-line” mode we noticed in practice

• An I.D. is output for each questioned toolmark• This is a computer “match”

• What’s the probability it is truly not a “match”?

• Similar problem in genomics for detecting disease from microarray data• They use data and Bayes’ theorem to get an

estimateNo diseasegenomics = Not a true “match”toolmarks

How good of a “match” is it?Efron Empirical Bayes’

Empirical Bayes’• We use Efron’s machinery for “empirical

Bayes’ two-groups model”Efron

• Surprisingly simple!

• Use binned data to do a Poisson regression

• Some notation:

• S-, truly no association, Null hypothesis

• S+, truly an association, Non-null hypothesis

• z, a score derived from a machine learning task to I.D. an unknown pattern with a group• z is a Gaussian random variate for the Null

Empirical Bayes’• From Bayes’ Theorem we can getEfron:

Estimated probability of not a true “match” given the algorithms' output z-score associated with its “match”

Names: Posterior error probability (PEP)Kall

Local false discovery rate (lfdr)Efron

• Suggested interpretation for casework:• We agree with Gelaman and ShaliziGelman:

= Estimated “believability” of machine made association

“…posterior model probabilities …[are]… useful as tools for prediction and for understanding structure in data, as long as these probabilities are not taken too seriously.”

Empirical Bayes’• Bootstrap procedure to get estimate of the KNM distribution of

“Platt-scores”Platt,e1071

• Use a “Training” set

• Use this to get p-values/z-values on a “Validation” set

• Inspired by Storey and Tibshirani’s Null estimation methodStorey

z-score

From fit histogram by Efron’s method get:

“mixture” density

We can test the fits to

and !

What’s the point??

z-density given KNM => Should be Gaussian

Estimate of prior for KNM

• Use SVM to get KM and KNM “Platt-score” distributions

• Use a “Validation” set

Posterior Association Probability: Believability Curve

12D PCA-SVM locfdr fit for Glock primer shear patterns

+/- 2 standard errors

Bayesian over-dispersed Poisson with intercept on test setBayesian Poisson with intercept on test set

Poisson (Efron) on test set Bayesian Poisson on test set

Bayes Factors/Likelihood Ratios

• In the “Forensic Bayesian Framework”, the Likelihood Ratio is the measure of the weight of evidence.• LRs are called Bayes Factors by most statistician

• LRs give the measure of support the “evidence” lends to the “prosecution hypothesis” vs. the “defense hypothesis”

• From Bayes Theorem:

Bayes Factors/Likelihood Ratios

• Once the “fits” for the Empirical Bayes method are obtained, it is easy to compute the corresponding likelihood ratios.o Using the identity:

the likelihood ratio can be computed as:

Bayes Factors/Likelihood Ratios • Using the fit posteriors and priors we can obtain the likelihood ratiosTippett, Ramos

Known match LR values

Known non-match LR values

Empirical Bayes’: Some Things That Bother Me

• Need a lot of z-scores• Big data sets in forensic science largely don’t exist

• z-scores should be fairly independent• Especially necessary for interval estimates around

lfdrEfron

• Requires “binning” in arbitrary number of intervals• Also suffers from the “Open-set” problem• Interpretation of the prior probability for this

application• Should Pr(S-) be 1 or very close to it? How close?

How to Carry Out a “Successful” Research Project

The Synergy Between Practitioners and Academia

Collaboration

• Practitioners:• Think about what questions you want to be able to

answer with data BEFORE experimentation• Write down proposed questions/design

• Be aware that the questions you want answers too MAY NOT have answers• What can you answer??

• Be aware that a typical research project takes 1-2

years to complete

Collaboration

• Practitioners:• Research projects are NOT just for interns!

• Interns typically need tremendous supervision for scientific/applied statistical research

• Take a college course on statistics/experimental design• Rate-my-professor is your friend!

• Visit local university/company websites to look for the outside expertise you may need.• Visit the department, go to some seminars

Collaboration

• Academics/Research consultants:• Be aware practitioners cannot just publish

whenever and whatever they want• Long internal review processes!

• COMMUNICATION!!!!!• Listen carefully to the needs/questions of

collaborating practitioners• Negotiate the project design

• What kind of results can be achieved within a reasonable amount of time?

• Hold regular face to face meetings if possible

Collaboration

• Academics/Research consultants:• Applied research is not just for

undergraduates/high-school interns!• Visit the crime lab!!!!!

• Watch the practitioners do their job.• Learn the tools they use day to day!

• Microscopy!!!!!

• Use their accumulated experience to help guide your design/desired outcomes• What do they focus on??

Fire Debris Analysis Casework

• Liquid gasoline samples recovered during investigation:• Unknown history

• Subjected to various real world conditions.

• If an individual sample can be discriminated from the larger group, this can be of forensic interest.

• Gas-Chromatography Commonly Used to ID gas.• Peak comparisons of chromatograms difficult and time

consuming.• Does “eye-balling” satisfy Daubert, or even Frye .....????

• 2D PCA• 97.3% variance retained

• Avg. LDA HOO correct classification rate: 83%

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Accidental Patterns on Footwear

• Shoe prints contain marks and patterns due to various circumstances that can be used to distinguish one shoe print from another.

• How reliable are the accidental patterns for identifying particular shoes?

-7.5

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3D PCA59.7% of variance

Facial Recognition Approach to Accidental Pattern Identification

Tool marks• Like shoes, tools can leave marks

which can be used in identification

• Class characteristics

• Subclass characteristics

• Individual characteristics

Standard Striation PatternsMade with ¼’’ Slotted Screwdriver

Measure lines and grooves with ImageJ

Translate ImageJ data to a feature vector that can be processed

A, 2, #2Bromberg, Lucky

C, 8, #4Bromberg, Lucky

LEA Striations

Questioned Documents: Photocopier Identification• Mordente, Gestring, Tytell

050 010 0015 0020 0025 00

50 010 0015 0020 0025 0030 00

050 010 0015 0020 0025 00

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050 010 0015 0020 0025 00

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Photocopy of a blank sheet of paper

Dust: Where does it come from?

Any matter or substance: • both natural and synthetic • reduces into minute bits, pieces, smears, and residues • encountered as trace aggregates

Our Environments!

Evidence!

N. Petraco

Where can you find it?

Everywhere

HouseWork

OutdoorsVehicle

N. Petraco

Analyze Results3D PCA-Clustering can show potential for discrimination

Bayes Net for Dust in Authentication Case

References• Bolton-King, Evans, Smith, Painter, Allsop, Cranton. AFTE J 42(1),23 2010

• Artigas. In: Optical Measurement of Surface Topography. Leach ed. Springer, 201l

• Helmli. In: Optical Measurement of Surface Topography. Leach ed. Springer, 2011

• deGroot. In: Optical Measurement of Surface Topography. Leach ed. Springer, 201l

• Efron, B. (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. New York: Cambridge University Press.

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Acknowledgements

• Professor Chris Saunders (SDSU)

• Professor Christoph Champod (Lausanne)

• Alan Zheng (NIST)

• Research Team:

• Dr. Martin Baiker

• Ms. Helen Chan

• Ms. Julie Cohen

• Mr. Peter Diaczuk

• Dr. Peter De Forest

• Mr. Antonio Del Valle

• Ms. Carol Gambino

• Dr. James Hamby

• Ms. Alison Hartwell, Esq.

• Dr. Thomas Kubic, Esq.

• Ms. Loretta Kuo

• Ms. Frani Kammerman

• Dr. Brooke Kammrath

• Mr. Chris Lucky

• Off. Patrick McLaughlin

• Dr. Linton Mohammed

• Mr. John Murdock

• Mr. Nicholas Petraco

• Dr. Dale Purcel

• Ms. Stephanie Pollut

• Dr. Peter Pizzola

• Dr. Graham Rankin

• Dr. Jacqueline Speir

• Dr. Peter Shenkin

• Mr. Chris Singh

• Mr. Peter Tytell

• Mr. Todd Weller

• Ms. Elizabeth Willie

• Dr. Peter Zoon

Website: Data, codes, reprints and preprints:

toolmarkstatistics.no-ip.org/

[email protected]

mailto:[email protected]

CCI Firearms and Toolmark Examiner Academy

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