Combining Fluid Dynamics, Statistics and Pattern 

Recognition in Bloodstain Pattern Analysis, to 

Quantify Spatial Uncertainty and Remove Human 

Bias, 

by Attinger (ISU), De Brabanter (ISU), SF Chang 

(Columbia U) and Gillen (NIST)
How was the crime committed?
Team

Attinger Daniel

Iowa State U

Fluid Mechanics and 

Transport Phenomena

Kris de Brabanter

Iowa State U

Statistical Learning and 

Mathematical Statistics

Shih-Fu Chang

Columbia U

Computer Vision and 

Machine Learning

Greg Gillen

NIST

Trace analysis and 

Inkjet Printing
4

Finertial

Fcapillary

Fdrag=Finertial+Fviscous

Fgravity

Fcapillary

Fviscous

Fgravity+Finertial

D. Attinger, C. Moore, A. Donaldson, A. Jafari, and H. A. Stone, Forensic Sci Int, 2013.
Brutin JFM 2011 5

Hulse-Smith, JFS 2005

Laber, IABPA news, 2008 

Evaporation of sessile drops

Coagulation

Rheology of complex fluid

Imbibition

Atomization

Instabilities

Biomechanics

Compressible flow

Spreading

Instabilities

Rheology

Trajectories

Drag

Heat transfer

Evaporation

FD BPA
6

Courtesy of H MacDonell, Oct 2010

Width/Length= sina

a

Objective 1: Quantifying the uncertainty in the region of origin 
Objective 1: The team will develop algorithms for identification of the 

region of origin of a blood spatter that propagate the uncertainty and 

represent its spatial magnitude in a 3-D space

In year 1, the team will extend to 3D the propagation method of Camana [FSI 2013], 

which determines how uncertainties in the measurement of individual stains 

influence the determination of the region of convergence of a blood spatter, 
8
9

Stain volume by integration of droplet height

Drop volume proportional to stain volume.

Drop velocity from spreading correlations.
10

Y-error, m Z-error, m

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Error, m

Gravity and Drag

Gravity

Straight Lines

Y-error, m Z-error, m

-2

-1.5

-1

-0.5

0

0.5

1 Error, m

Gravity and Drag

Gravity

Straight Lines

Y-error, m Z-error, m

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Error, m

Gravity and Drag

Gravity

Straight Lines

Y-error, m Z-error, m

-12

-10

-8

-6

-4

-2

0

2

4

Error, m

Gravity and Drag

Gravity

Straight Lines

Y-error, m Z-error, m

Gravity and Drag

Gravity

Straight Lines
ASME 2016 ICNMM, July 10 -14, 2016 target material influences spreading

11

Glass

Al mirror

Polycarbonate 

bare

Al-600grit

Cardstock

Polycarbonate

20grit

Substrate Correlation

Glass b=0.8474(Re2Oh)0.1334

Aluminum mirror b=0.4285(Re2Oh)0.1934

Aluminum 600 grit b=0.3914(Re2Oh)0.1965

Cardstock b=0.4019(Re2Oh)0.1921

Polycarbonate bare b=0.3428(Re2Oh)0.2118

Polycarbonate 20 grit b=0.3954(Re2Oh)0.1912

• Correlations for each surface was evaluated based on the 

experimental results

• The spreading factor on glass at low Re2Oh was more than 25% 

higher than those on other surface due to the static contact angle. 

• The spreading factor on roughened surface was lower

2 0 166 0 61 .  

. (Re Oh )

Global correlation suggested by 

Bousfield at al. (1995)

S. Kim et al., Forensic Sci Int, 2013.
Objective 2: develop robust pattern recognition algorithms to identify the 

generation mechanism of an unknown bloodstain pattern

The classification will rely on a database of spatters produced under known conditions –

including gunshot, cast-off, dripping blood, transfer stains. This will advance the state of 

the art, where classification is done by the human investigator. Here again the 

classification algorithm will provide automated interpretation and uncertainty 

quantification (Chang and De Brabanter).
Pattern recognition for bloodstain identification

• “Big data” problem since the number of bloodstain in a crime scene is (can be) 

massive

• So far, relatively simple classifiers have been used (Naive Bayes, KL, LDA, QDA)

 Simple and easy to use classifiers

 Can handle “Big data”

• Rely on parametric assumptions on the distribution of the data (usually 

normality)

• Most of them produces linear decision boundaries.

In this proposal we want to use state-of-the-art classification algorithms (SVM, 

random forest, etc.) suitable for large data sets while relaxing the underlying 

parametric distribution assumption of the data. These classifiers can produce linear 

and nonlinear decision boundaries.

We will use a technique called fixed-size least square support vector machines (De 

Brabanter et al. (2010)) to classify blood spatters. This technique is capable of 

handling several millions of data points on standard PCs.

De Brabanter K., De Brabanter J., Suykens J.A.K., De Moor B., Optimized Fixed-Size Kernel Models for Large Data Sets, 

Computational Statistics & Data Analysis, vol. 54, no. 6, Jun. 2010, pp. 1484-1504
How the research will expand on NIST work

NIST Collaborator Dr. Greg Gillen is a Supervisor Research Chemist and Group Leader at 

NIST.

Dr. Gillen invited Attinger for a talk and research discussion in 2003, on droplet 

generation for analytical purposes. 

The two scientists have maintained a scientific correspondence ever since. Dr Gillen has 

expertise related to trace analysis and inkjet printing, both domains being relevant to the 

estimation of drop impact conditions for trajectory reconstruction.

Gillen will develop expertise in 

1. inkjet printing of bloodstains of controlled volume on e.g. fabric, to assist with 

stain formation on absorbing surfaces.

2. characterization of the volume of a blood droplets using analytical method, to 

provide initial conditions for the trajectory reconstruction algorithms of Attinger
Goals are to produce rational and automatic methods for bloodstain pattern 

analysis, 

to allow the rational estimation of the uncertainty, and 

the rational identification of the spatter mechanism
Process for inserting the work into the crime 

laboratory

Currently, the reconstruction of trajectories relies on manual selection of stains; 

the identification of the mechanism causing a blood spatter (gunshot, beating, 

transfer) relies on the judgement of a human analyst.

The proposed research, if funded, will automatize both processes and remove (or 

strongly reduce) human intervention and bias.

PI Attinger has taught advanced bloodstain pattern analysis to the Crime Scene 

Analysts of Las Vegas/Henderson, in the graduate program of the University of the 

West Indies, and is presently setting up a program to educate criminal police 

forces in Switzerland. He is a court-certified expert in bloodstain pattern analysis. 

He has the outreach and contacts to expose crime laboratories to these new 

techniques, when ready. 
ASME 2016 ICNMM, July 10 -14, 2016

A. Kolbasov, P. Comiskey, R. P. Sahu, S. Sinha-Ray, A. L. Yarin, B. S. Sikarwar, S. Kim, T. Z. Jubery, and D. Attinger, 17

"Blood Rheology in Shear and Uniaxial Elongation," Rheological Acta, under revision 2016.

Blood viscosity: viscoelasticity
Application of statistics/probability to reducing 

or clarifying uncertainty

Current methods to identify the region of origin of a blood spatter neglect key physics 

such as gravity and drag, and do not allow rational estimation of the uncertainty. 

Very recently, physics-based algorithms have been developed to predict the region of 

origin of the stains by inspection of a blood spatter [5-7].

This is a significant advance which makes it possible to estimate and propagate 

uncertainties in the determination of the region of origin. The team will therefore 

develop algorithms for identification of the region of origin of a blood spatter that 

propagate the uncertainty and represent its spatial magnitude in a 3-D space (Objective 

1).
Brief motivation

Identifying the 3D region of origin of a blood spatter in a crime scene involves the 

backward reconstruction of trajectories of stains selected among the 10-1000+ 

stains of a spatter. 

Currently used reconstruction methods assume straight trajectories, hereby 

neglecting the effect of drag and gravity on the droplet’s motion. 

This oversimplification introduces significant and difficult/impossible-to-quantify 

uncertainties in the estimation of the region of origin : ”Such errors are significant 

enough to wrongly conclude that a person was standing when in fact they may 

have been sitting” [ref. 15 of proposal].

Also, Bloodstain Pattern Interpretation still relies on human comparison between 

the patterns observed at the crime scene with those learned in BPA classes [4]. 

PI Attinger at Iowa State has developed methods [5-7] to include drag and gravity 

in the reconstruction of trajectories. Collaborator SF Chang (H-index 93) and 

Brabanter (ISU) are experts in pattern recognition.