The evaluation of fingermarks given activity level propositions

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Delft University of Technology

The evaluation of fingermarks given activity level propositions

de Ronde, Anouk; Kokshoorn, Bas; de Poot, Christianne J.; de Puit, Marcel

DOI

10.1016/j.forsciint.2019.109904

Publication date

2019

Document Version

Final published version

Published in

Forensic Science International

Citation (APA)

de Ronde, A., Kokshoorn, B., de Poot, C. J., & de Puit, M. (2019). The evaluation of fingermarks given

activity level propositions. Forensic Science International, 302, [109904].

https://doi.org/10.1016/j.forsciint.2019.109904

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Original

Research

Paper

The

evaluation

of

ﬁngermarks

given

activity

level

propositions

Anouk

de

Ronde

a,b,c,

*

,

Bas

Kokshoorn

c

,

Christianne

J. de

Poot

a,b,d

,

Marcel

de

Puit

c,e

a_Amsterdam_University_of_Applied_Sciences,_Weesperzijde_190,₁₀₉₇_DZ_Amsterdam,_The_Netherlands b_VU_University_Amsterdam,_De_Boelelaan_1105,₁₀₈₁_HV_Amsterdam,_The_Netherlands

c

NetherlandsForensicInstitute,LaanvanYpenburg6,2497GBTheHague,TheNetherlands

d

PoliceAcademyoftheNetherlands,P.O.Box348,7301BBApeldoorn,TheNetherlands

e

DelftUniversityofTechnology,VanderMaasweg9,2629HZDelft,TheNetherlands

ARTICLE INFO Articlehistory: Received3May2019

Receivedinrevisedform2July2019 Accepted9July2019

Availableonline30July2019 Keywords: Touchtraces Evidenceinterpretation Activity Bayesiannetwork ABSTRACT

Fingermarksarehighlyrelevantincriminalinvestigationsforindividualizationpurposes.Insomecases, thequestionincourtchangesfrom‘Whoisthesourceofthefingermarks?’to‘Howdidthefingermark enduponthesurface?’.Inthispaper,weexploretheevaluationoffingermarksgivenactivitylevel propositions byusingBayesian networks. Thevariables that provide informationon activitylevel questionsforfingermarksareidentifiedandtheircurrentstateofknowledgewithregardstofingermarks isdiscussed.Weidentifiedthevariablestransfer,persistency,recovery,backgroundfingermarks,location ofthefingermarks,directionofthefingermarks,theareaoffrictionridgeskinthatleftthemarkand pressuredistortionsasvariablesthatmayprovideinformationonhowafingermarkendedupona surface.Usingthreecaseexamples,weshowhowBayesiannetworkscanbeusedfortheevaluationof fingermarksgivenactivitylevelpropositions.

1.Introduction

Fingermarksplayanimportantroleinforensicscience.Based

ontheassumptionthateveryindividualholdsauniquepatternof

frictionridgeskin ontheirhands, this pattern canbe usedfor

identiﬁcation.Bydeterminingthesourceoftheﬁngermark,alink

betweenthedonorandacrimescenecanbeestablished.Thereisa

wealthofresearchonthevisualizationof latentﬁngerprintsin

ordertoenhancethefrictionridge patternforindividualization

purposes[1,2].Whilethistypeofresearchisveryvaluableforthe

individualizationofthesourceofatrace,theﬁngermarkitselfmay

notunequivocallybeattributedtoacriminalactivity.

An important question that often comes up in court cases

regardingforensicevidenceistodeterminehoworwhenatrace

was deposited.Consider the following case example;a woman

calls thepolicetoreport thatthere hasbeena burglaryinher

apartment.Thepoliceﬁndfourﬁngermarksontherailingofthe

balcony, which leads to the assumption that the perpetrator

enteredtheapartmentviathebalcony.Throughadatabasesearch,

amatchisfoundwithasuspect,whoisanacquaintanceof the

woman. The suspect claims that, instead of an unauthorized

intrusionviathebalcony,hevisitedthewomanaweekearlierand

smokedacigaretteonthebalconywhileleaningontherailing.In

cases likethis, thequestionatstake changesfrom ‘Whois the

sourceoftheﬁngermarks?’to‘Whatactivityledtothedeposition

oftheﬁngermarks?’,whichrequiresadifferentassessmentofthe

ﬁndings.

When investigating forensic evidence, a forensic scientist

formulates a set of propositions, usually representing the

prosecution and thedefense propositions.Cook, Evett, Jackson,

Jones and Lambert [3] propose three classes of propositions:

sourcelevel,activitylevelandoffencelevelpropositions.Inthe

balconycaseexample,theinvestigationshifts fromdetermining

thesourceoftheﬁngermarkstoaddressingtheactivitythattook

place.IntheforensicexpertiseﬁeldsofDNA,ﬁbres,glass,paintand

gunshotresidues,evaluationoftheevidencegivenactivitylevel

propositions is already being studied [4]. However, for

ﬁnger-marks,thistopicisnotyetexplored.

Therearemanyvariablesthatmayprovideinformationonhow

a ﬁngermark was deposited on a surface. In the balcony case

example,wherethequestionnowiswhetherthesuspectclimbed

thebalconyorthesuspectsmokedacigaretteonthebalconyand

leaned on the railing, variables such as the location of

theﬁngermarks,andthedirectionoftheﬁngermarksmayprovide

information on the activity that took place. In general, the

interpretation of evidence at activity level requires

more contextual information [3]. When multiple variables

* Correspondingauthorat:AmsterdamUniversityofAppliedSciences, Weesperzijde190,1097DZAmsterdam,TheNetherlands.

E-mailaddresses:a.de.ronde2@hva.nl,a.de.ronde@nﬁ.nl(A.deRonde). http://dx.doi.org/10.1016/j.forsciint.2019.109904

ContentslistsavailableatScienceDirect

Forensic

Science

International

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inﬂuencetheinterpretationoftheevidence,itcanbedifﬁcultto

taketheirdependenciesintoaccountina directcalculationofa

likelihoodratio[5].

A methodthat iscommonlyusedforcaseswhereadditional

factorsplayaroleisaBayesiannetwork.ABayesiannetworkisa

graphicalrepresentationofamathematicalmodelwhichcanbe

usedtoevaluatetheﬁndings,particularlyifthereisadependency

betweenrelevant variables[4]. A Bayesian network consistsof

nodes,directedarcsandprobabilityassignmentsofthenodes.It

can for instance be used to computea likelihood ratio of the

evidence given the prosecution proposition and the defense

proposition,basedonallvariablesthatareconsideredrelevantin

theinterpretationoftheevidence.ThismakesBayesiannetworks

anappropriatemethodtoevaluateevidencegivenpropositionsat

activitylevelwithintheﬁeldofforensicscience.AlthoughBayesian

networks have been proposed to interpret ﬁngermarks given

sourcelevelpropositions[6],theyhavenotbeenusedtoevaluate

ﬁngermarksgivenactivitylevelpropositions.

In thispaper,we describeaframeworkfortheevaluationof

ﬁngermarks given activity level propositions using Bayesian

networks.Wediscussthevariablesthatprovideinformation on

ﬁngermarksatactivitylevel,followedbythreecaseexamplesfor

whichBayesiannetworksarecreated.Weultimatelyelaborateon

possibledirectionsforfurtherresearchonthistopicsuchthatthe

proposedframeworkcouldbeoptimallyappliedincasework.

2.Relevantvariables

Inthissection,weexplorethevariablesthatprovide

informa-tiononﬁngermarkswithregardstoactivitylevelpropositions.We

donotdiscussvariablesrelatedtosourcelevelpropositionssince

determiningthedonorofaﬁngermarkisconsideredoutsidethe

scopeofthisstudy.Furthermore,weassumedthatifaﬁngermark

ispresent,thedonoractuallytouchedtheitem.1_Touching_a_surface

canbeseen as anactivity in itself,and therefore activity level

propositionsmaydisputewhetherthesurfaceisactuallytouched

ortheﬁngermarkisaresultofforgery[1].Anotherdisputemay

focusonthecircumstancesofhowtheﬁngermarkisrecovered,for

instancewhenthereareissueswiththechainofcustody[7].These

types of propositions are considered outside thescope of this

paper by assuming the surface is actually touched when a

ﬁngermarkispresent.

Wedividedtherelevanteventsthatprovideinformationonthe

activitythatledtodepositionoftheﬁngermarksintwogroupsof

variables:‘ﬁngermarkformationprocess’,and‘mannerof

deposi-tion’. The group ‘ﬁngermark formation process’ represents the

factorsthat relatetotherequirementsof ﬁngermarkformation,

visualizationandrecovery.Thevariablesidentiﬁedinthisgroup

arethetransfer,persistenceandrecoveryofﬁngermarksandthe

backgroundlevelsofﬁngermarksalreadypresentonanitem.The

group_‘mannerofdeposition_’representsthefactorsthatrelateto

howthedonordepositedtheﬁngermark.Thevariablesidentiﬁed

inthisgrouparethepositionofthehandduringplacement,the

locationofthe_{ﬁngermarks,}areaoffrictionridgeskinthatleftthe

mark,thedirectionoftheﬁngermarksandthepressureappliedto

thesurfaceduringdeposition.

2.1.Fingermarkformationprocess

2.1.1.Transfer

Aconsequenceofanactivitymaybethetransferofmaterialtoa

surfacebyaﬁnger,creatingaﬁngermark.Untilnow,researchon

thetransferofﬁngermarksfocusedmostlyonthecompositionof

the residue for the purpose of enhancing the quality of the

ﬁngermarkforindividualizationatsourcelevel[8].However,the

guidelines of theENFSI [9] show that transfer is animportant

variable to consider when looking at the scientiﬁc ﬁndings in

relationtoactivities.

Fingermarks have advantages over other types of forensic

evidence.Fingermarksareconsideredtobeaproofofcontactdue

toadirecttransferoftheridgedetailtoasurface.Furthermore,

ﬁngermarkscannottransferindirectlyviasurfacesorindividuals

unlessgreateffortismade[10].Secondaryorfurthertransferof

ﬁngermarksisgenerallynottakenintoaccount(pleasenotethe

exception of ﬁngermarks on tape [11]). These are important

advantagesoverDNA,sinceDNAcantransferindirectlyandeven

retransfer from onelocation toanother [12]. Althoughindirect

transferisgenerallynotapplicabletoﬁngermarks,transferisstill

animportantvariabletoconsidersincetheprobabilityoftransfer

ofaﬁngermarkmaydifferbetweenactivities.

The transfer of ﬁngermarks depends on several factors: the

nature of the surface, the deposition conditions and donor

characteristics [8,13,14]. The deposition conditions such as

pressure and duration of contact may vary between activities,

and this may result in different transfer probabilities. If the

pressureofthehandonthesurfaceishigher,theprobabilityof

transfermightbehigher[13].Thepropositionsoftheprosecution

andthedefensemaysuggestdifferentlevelsofpressureneededto

conduct the proposed activities, leading to the assignment of

different transfer probabilities. This is also true for other

depositionconditions,whichmaketheobservedtransfer(orthe

absencethereof)more orlessprobablegivendifferent

proposi-tions.However,thedevelopmentandrecoveryofﬁngermarksona

surface depend on more than the mechanisms of transfer;

variables such as persistence and recovery also inﬂuence the

probabilityofrecoveringﬁngermarks.

2.1.2.Persistence

Aﬁngermarkmaynotberecoveredinthesameconditionasit

was deposited. This is due to degradation, the process during

which the initial composition of a ﬁngermark changes after

deposition[8].Degradationwilloccurfromthetimethe

ﬁnger-markhasbeendeposited,tothesubsequentevidencerecoveryand

mayaffectthepersistenceofaﬁngermark.Thedegradationofa

ﬁngermarkisinﬂuencedbythe‘triangleofinteraction’,consisting

of the ﬁngermark composition, the nature of the surface and

environmentalconditions[2].Forthenatureofthesurfaceitis

knownthatﬁngermarkcompoundsmaybeabsorbedbysurfacesof

porousmaterial,whereastheystayonthesurfaceofnon-porous

materials.Thissurfaceinteractionmayinﬂuencethedegradation

oftheﬁngermarks[15].Furthermore,environmentalfactorslike

temperature,light, humidity and air circulationhave shown to

in_ﬂuencethedegradationof_ﬁngermarksovertime[14].

It isgenerallynotexpectedthat thenatureof thesurfaceis

disputedbetweenactivitylevelpropositionssincethesamesetof

ﬁngermarks on the same item is questioned under both

propositions(unlessthere isanissuewiththechain-of-custody

[7]).However,environmentalconditionsmayvarybetweenapair

of activity level propositions for ﬁngermarks, for example, if

propositionsdisputethemomentwhentheﬁngermarkisleftand

thus thetime interval betweenthemoment of depositionand

recovery. During that time interval, the ﬁngermarks could be

subjectedtodifferentenvironmentalconditions.Inthatcase,the

factorpersistenceplaysasigniﬁcantrole.

2.1.3.Recovery

Aftertransfertoandpersistenceonasurface,theﬁngermark

mustbedetectedandrecoveredfromthecrimescene.Thisprocess

1

Onacrimescene,fingermarkscanbefoundonitemsandfixedsurfaces.Inthis article,weusethetermitemforboth,unlessfurtherspecified.

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isdescribedbythevariablerecovery.Fingermarkscanbelatent,

meaning that they must be visualized with the use of an

enhancementtechnique.Severalfactorsinﬂuencethesuccessrate

ofthedetectionofaﬁngermark. Thesensitivityoftheavailable

methodstovisualize ﬁngermarksvaries [16],meaning thatnot

every technique has the same success rate. Furthermore, an

incorrect choice of technique, an incorrect application of a

technique or applying multiple techniques in thewrong order

can result in lower success rates of ﬁnding a ﬁngermark [17].

Anotherfactorinﬂuencingtherecoveryprobabilityistargetingof

the correct location. Fingermarks could be missed by a wrong

selectionoflocationstosampleonthecrimescene,resultingina

different probability torecover ﬁngermarks. Other factors that

impactontheprobabilityofrecoveryarethelevelofbackground

marksthat arealready present, and the criteria established to

determinewhetheraﬁngermarkissuitableforindividualization.

Forexample,ifpartial ﬁngermarksarepresent, thesewillmost

likelynot berecoveredif theyarenotof valueforcomparison.

However,whenthequestioniswhetherthesuspectworegloves,

thepresenceofthesepartialﬁngermarksmayverywellinﬂuence

the interpretation at activitylevel. As a result, the probability

to recover ﬁngermarks may vary between the activity level

propositionsatstake.

2.1.4.Combinationoftransfer,persistenceandrecovery

Allthreevariablestransfer,persistenceandrecoveryinﬂuence

the probability of the ﬁndings separately, but they cannot be

clearly separated. If no ﬁngermark is recovered, it does not

automatically mean that the ﬁngermark was not present

(transfer).Theﬁngermark couldhavebeen degradedsuchthat

visualizationwasnotpossible(persistence),thechosen

enhance-ment technique could have been unsuccessful(recovery) or it

maybetheresultofa combinationof thesefactors.Therefore,

thesevariablesareoftentakentogetherandasingleprobabilityis

assignedtotheﬁndings.

2.1.5.Backgroundﬁngermarks

Thereareoftenalreadyﬁngermarkspresentonitemsthatare

unrelated to the activities at stake. This means that the

ﬁngermarkscouldhavealreadybeenpresentontheitembefore

the alleged activity took place or may have ended up onthe

surfaceafter theallegedactivitiestookplace.Fingermarksthat

aretransferredtothesurfacebyactionsunrelatedtotheactivities

atstakeareconsideredasbackgroundﬁngermarks.Consider,for

example,thattheissueiswhetherasuspectstabbedthevictim

withaknifeorthatanunknownpersonstabbedthevictimwith

theknife.Sayweﬁndﬁngermarksofthesuspectonthehandle,as

wellassomeﬁngermarksofoneormoreunknownindividuals.

Nowthe weight of theevidence given these two propositions

woulddependontherelationthatthesuspecthaswiththeitem

(e.g. could he have handled the knife prior to or after the

incident?),butalsoontheprobabilitythatweﬁndbackground

ﬁngermarksonthehandleofthisspeciﬁcknife.Iftheknifewas

cleanedrecently,thatprobabilitymaybelowandtherecoveryof

ﬁngermarksofanunknownindividualmaysupportthesuspect's

proposition.However,ifwehaveahighexpectationofrecovering

backgroundﬁngermarks(forinstancebecausetheknifeisnota

personalitemandwasincommonuse)theobservedﬁngermarks

of unknown individual(s) may be neutral towards the two

propositions. The probability thatthese unknown ﬁngermarks

belongtobackground levelsofﬁngermarksontheitemshould

thereforebetakenintoconsideration.Duringinvestigation,itis

therefore important to consider the general activities that

occurred priorto orafter the allegedactivities that mayhave

resultedinﬁngermarksontheitem.

2.2.Mannerofdeposition

2.2.1.Positionofthehandandﬁngersduringdeposition

Thewayinwhichtheﬁngermarksaredepositedonasurface

depends on the positioning of the hand and ﬁngers during

deposition.Thepositionofthehandandﬁngersonanitemmay

differbetweenactivities,whichisdeterminedbythepurposeof

the activity, theanatomy of the humanbody and thephysical

characteristicsoftheitem.

The anatomy of the human body causes restrictions in

movements ofthelimbs.Duetotheserestrictions,thepossible

positions of the hand and _ﬁngers on an item are limited. The

physicalcharacteristicsoftheitemalsoinﬂuencethepositionof

thehandandﬁngersonanitem.Thesecharacteristicsincludesize,

weight,shape,structure,typeofmaterial,itsfunctionetc.Consider

thatsomeonegraspsaknifeforstabbing:heorshemostlikely

grabstheknifeatthehandleduetotheshapeandstructureofthe

knife. The physical characteristics of the handle of the knife

inﬂuence thepositioning of the hand and ﬁngers, as may the

purposeoftheactivity:cuttingapieceofbreadversusstabbing

mayforinstanceaffectthewaytheknifeisheld.

Sincethemovements,thephysicalcharacteristicsoftheitem

and the goal of the activity may differ between activities,the

positionofthehandandﬁngersprovidesinformation thatmay

assistinevaluatingtheﬁndingsgivenactivitylevelpropositions.

Sinceitcanbedifﬁculttodescribethepositionofthehandand

ﬁngersdirectly,wedescribethepositionofthehandandﬁngers

duringdepositionthroughfourvariables:locationofthe

ﬁnger-marks,directionoftheﬁngermarks,partofthehandthatleftthe

ﬁngermark,andpressure.

2.2.2.Locationoftheﬁngermarks

The position of the hand and ﬁngers on an item during

depositioninﬂuencesthelocationoftheﬁngermarksontheitem.

deRonde,vanAken,dePuitanddePoot[18]designeda model

that can be used to analyzethe location of ﬁngermarks on

2-dimensionalitemsgivendifferentactivities.Withtheuseofthis

model,pillowcasescouldbeseparatedinthetwoactivityclasses

smotheringandchanging,basedonthelocationoftheﬁngermarks

onthepillowcases.Thisshowsthatthelocationofﬁngermarkson

anitemprovidesinformationontheactivitythatthedonorcarried

out,andisthereforeanimportantvariabletotakeintoaccount.

2.2.3.Directionofaﬁngermark

Whentouchingasurface,thehandandﬁngersarepositionedin

a certain direction. This direction varies between different

activitiesandassuchmaybedistinctiveforparticularactivities.

Inthebalconycaseexample,theﬁngermarkdirectionasaresultof

climbing the balcony may be different from the ﬁngermark

directionasaresultofleaningontherailing.Thevariabledirection

is used bycrime scene of_ﬁcers tomake inferences during the

investigationphaseonacrimescene.Anexampleofthisisthat

ﬁngermarks found pointing inwards on theinside of a broken

windowframeareoftenconsideredtoberelatedtotheactivityof

climbingthroughawindowduringaburglary.However,thereare

nostudiesthatreportonthedirectionofﬁngermarksinrelationto

activities. Theprobability toﬁndacertain ﬁngermarkdirection

underthedifferentpropositionsmayprovideinformationonthe

activitylevel.

2.2.4.Areaoffrictionridgeskin

Differentactivitiesrequiretheuseofdifferentpartsofthehand

andthereforetheareaoffrictionridgeskinthatleftaﬁngermark

mayprovideinformationontheactivity.Considerthebalconycase

example: itmaybemore probabletorecovera complete palm

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the suspect simply touched the railing while standing on the

balcony.Theareaoffrictionridgeskinthatleftthemarkcanbe

determinedwhenthedonorofthe_ﬁngermarkisknown.Incases

where a suspect or a corresponding reference print is absent,

determiningtheareathatlefttheprintmaybedifﬁcult.

Although recent research has focused on determining

whetheritwasa left-hand or a right-handthatdeposited an

individualfingerprint [19–21], assigning a specific finger to a

ﬁngermark is still a topic for further research. Nevertheless,

forensic examiners are trained to nominate corresponding

ﬁngerstoﬁngermarks based onthe size,patterntype, shape,

etc.Thisinformationmightbeveryvaluablefortheevaluation

ofﬁngermarksgivenactivitylevelpropositions.Ifa likelihood

ratio can be determined on whether a recovered ﬁngermark

comesfrom a speciﬁc ﬁnger, or comes from another area of

friction ridge skin, this information can be used in the

evaluationof theﬁndings.

2.2.5.Pressure

Whenfrictionridgeskintouchesasurface,theshapeoftheskin

changesasaresultofthepressureappliedonthesurfaceandthe

pliabilityoftheskin.Maceo[22]identi_ﬁestwotypesofpressureof

aﬁngeronasurface:verticalpressureandhorizontalpressure.An

increasedverticalpressureresultsinmorepointsofcontactwith

the surface, causing a broader ﬁngermark [23]. Furthermore,

verticalpressureaffectsthewidthoftheridgesandthefurrowsina

ﬁngermark[24].Asaresult,thesizeofaﬁngermarkandthewidth

oftheridgesinaﬁngermarkmayprovideinformationaboutthe

verticalpressureapplied.However,weexpectthatitwillbevery

difﬁculttodeterminetheverticalpressureappliedtoasurfaceby

justlookingattheﬁngermark,sincethesizeofaﬁngermark,the

widthoftheridgesandtheconditionoftheskinvariesgreatly

betweendonors.

Pressure inthe horizontalplanecausesdeformationof the

skinthat mayresult ina distortion of theﬁngermarks in the

formofsmearsorswipes[22].Thispressuredistortionisoften

directional,andthedistortionseldommovesintwodirections

[22,24].Studyingthesedirectionaldistortions ina ﬁngermark

canbeof greatervalueforthe interpretationat activitylevel.

Theprobabilityofdetectingapressuredistortioninaparticular

directionmaybedifferentfortwoactivitiesandthis

informa-tioncanbeusedintheassessment.Anotherpossibilityisthat

someactivitiesmayalwaysresultindistortedﬁngermarks.Ifthe

probability to obtain a distorted ﬁngermark differs for two

activities,thisinformationmaybeofgreatvaluefortheactivity

levelinterpretation.

3.Bayesiannetworkconstruction

Withthevariablesidenti_ﬁed,weshowtheimplementationof

theseinaBayesiannetwork.Inthispaper,wefocusonﬁngermark

gripspresent onanitem. Bya grip, werefer toa collectionof

ﬁngermarksforwhichitisassumedtheyareleftinoneandthe

sameplacementofthehand.Thismeanstheconsideredmarkscan

varyfromoneﬁngermarktoacompletehandmark,althoughthey

originatefromone andthesamehandand bedeposited atthe

same time. In this paper, we assume that the source of the

ﬁngermarks is identiﬁed or unknown. Recent literature on

ﬁngermarksatsourcelevelfocusonamoreprobabilisticapproach

to present the evidential strength of a match [1,25]. The

implementationofthis probabilisticsourcelevelinformationin

Bayesiannetworksisconsideredoutsidethescopeofthispaper;

wereferthereadertoTaroni,Biedermann,Bozza,Garbolinoand

Aitken[4].

WebuiltthreedifferentBayesiannetworks,eachbased ona

versionofthebalconycaseexampledescribedintheintroduction

ofthispaper.Intheﬁrstcaseexample,onegripisrecoveredonthe

railing and it is questioned whether the suspect climbed the

balconyorleanedonthebalcony.Thesecondcaseexamplefocuses

onthequestionofwhetherthesuspectclimbedthebalcony or

someoneelseclimbedthebalcony.Intheﬁnalcaseexample,the

implementationof multiple gripsis discussedfor thequestion

whetherthesuspectclimbedthebalconyorsomeoneelseclimbed

the balcony. All three networks werebuilt using the software

Hugin (version 8.6)2 _and _can _be _found _in _the _{supplementary}

material.Forthepurposeofillustration,weaddedsomeﬁctional

probabilities in the network for the ﬁrst case example. The

probabilitiesusedinthisexamplearesolelybasedoninformed

judgement of the authors, and are not based on any scientiﬁc

experimentsorpublisheddata.

Becausethepurposeofthispaperistoshowtheconstructionof

Bayesian networksfor theevaluationof ﬁngermarks atactivity

level,wedonotelaborateonhowthevariablescanbeobjectively

measured, nor do we aim to assign exact probabilities to the

network.Themainfocuswillbeontheconsiderationsaforensic

scientisthastomakewhencreatingaBayesiannetworktoevaluate

ﬁngermarksgivenactivitylevelpropositions.Inthediscussion,we

willelaborateonhowprobabilitiescanbeassignedtothenodes

andweproposetopicsforfurtherresearchthatwillgivesubstance

totheseprobabilityestimations.

Fig.1.Bayesiannetworkfortheevaluationofﬁngermarksatactivitylevelincaseexample1.

2

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3.1.Caseexample1:Natureoftheactivitydisputed

3.1.1.Backgroundinformation

Consider the balcony case example we described in the

introduction.Thepolicefoundagripofﬁngermarksontherailing

ofthebalcony,whichleadstotheassumptionthattheperpetrator

enteredtheapartmentviathebalcony.Thesuspect,foundthrough

adatabasesearch,claimsthathisﬁngerprintsarenotleftonthe

balcony due toan unauthorized intrusion via thebalcony, but

duringalegalvisittothewomanwhenleaningontherailingwhile

smokingacigarette.Thedisputeofthedefense isaimedatthe

natureoftheactivity[26],resultinginthefollowingactivitylevel

propositions:

Hp:Sclimbedthebalconyanddidnotleanontherailing.

Hd:Sleanedontherailinganddidnotclimbthebalcony.

FollowingtheprocessdescribedbyTaylor,Biedermann,Hicks

andChampod[27],weconstructedtheBayesiannetworkshownin

Fig. 1, using the same colouring scheme. Sections 3.1.2–3.1.7

describethenodes,thedependenciesandtheconsiderationsfor

thestatesofeachnode.Weconstructedthisnetworktoevaluatea

positiveresult,e.g.aﬁngermarkfoundonasurface.Ifnomarksare

recovered,theproposedBayesiannetworkwouldonlyconsistof

nodes [1] to [5], since determining the ﬁndings [6] to [12] is

impossible.

3.1.2.Node[1]propositions

TheblacknodePropositionsinFig. 1representsthemainactivity

level propositions. This node has two states, Hp and Hd,

representingrespectivelythepropositionoftheprosecutionand

the defense. Assignment of the prior probabilities is generally

outsidethedomainoftheforensicscientist.Forthepurposeofthis

example, we have assigned equal prior probabilities to each

proposition(Table1).

3.1.3.Nodes[2]Sclimbedthebalconyand[3]Sleanedontherailing

Thepropositionalnodeimpliestwoactivitynodes:Sclimbedthe

balconyand S leaned on the railing, denotedblue in Fig.1.We

deﬁnedthestates‘true’and‘false’tobothnodes.Theprobabilities

ofthestatesofnodeSclimbedthebalcony(Table2)andnodeS

leanedontherailing(Table3)areconditionedonthestatesofnode

propositions.Table2showsthatgiventhatHpistrue,thenodeS

climbed balcony is true with probability p=1 and false with

probabilityp=0.IfHdistrue,thenodeSclimbedthebalconyistrue

with probability p=0 and false with probability p=1. For the

probabilitytableofnodeSleanedontherailingshowninTable3,

thereverseholds.

3.1.4.Nodes[4]FingermarksSthroughclimbingand[5]FingermarksS

throughleaning

Asa resultoftheactivities climbing orleaning,ﬁngermarks

endedupontherailing.InFig.1,themechanismsbywhichthe

activitiesleadtotheﬁndingsarerepresentedbytheyellownodes

FingermarksSthroughclimbingandFingermarksSthroughleaning,

both with states _‘true’ and _‘false’. Within these nodes, the

combined probabilities of transfer, persistence and recoveryof

the ﬁngermarks as a result of the proposed activities are

considered.

Table4 showstheconditionalprobabilitytableforthenode

FingermarksSthroughclimbing.Thisnodedependsontheactivity

nodeSclimbedthebalcony.GiventhatSclimbedthebalconyistrue,

Padenotestheprobabilitytoobtainﬁngermarksgiventheactivity

climbing. This incorporates the probabilities for transfer, the

persistenceandtherecoveryofﬁngermarksontherailingthrough

climbing. From the fact that the states of nodes are mutually

exclusiveandexhaustivefollowsthattheprobabilitythatthereis

no transfer, persistence and recovery of ﬁngermarks through

climbing is equal to 1Pa. The probability table for the node

Fingermarksthroughleaningisconstructedinanequalmanner.

3.1.5.Node[6]direction

Oneaspectwecanobservefromtherecoveredﬁngermarksis

theirdirection.Thenodeforthisvariableisshownbythecolour

red in Fig. 1. Before the direction of the _ﬁngermarks can be

determined,thetransfer,persistenceandrecoveryofthe

ﬁnger-markshadtobesuccessful,whichmeansthatthenodeDirectionin

thenetworkisdependentontheprobabilitytoobtainﬁngermarks

undertheallegedactivities.ThisisshowninFig.1bydrawingan

arrowfromFingermarksthroughclimbingandFingermarksthrough

leaningtothenodeDirection.

There aremultiple options todeﬁne thestates of thenode

Direction;theoretically,everyanglecouldbeaseparatestate.Inour

caseexample,wechosetodeﬁnetwostatesforthedirectionofthe

ﬁngermarks:theﬁngermarksarepointinginwards(tothehouse)

andtheﬁngermarksarepointingoutwards(awayfromthehouse).

TheconditionalprobabilitytableofthenodeDirectionisshownin

Table 5. Assume that ﬁngermarks through climbing is true and

Table1

Priorprobabilitytableforthenode[1]PropositionsinFig.1.

Propositions Probability

Hp:Sclimbedthebalconyanddidnotleanontherailing. 0.5

Hd:Sleanedontherailinganddidnotclimbthebalcony. 0.5

Table2

Conditionalprobabilitytableforthenode[2]SclimbedthebalconyinFig.1.

Propositions Hp Hd

Sclimbedthebalcony:

True 1 0

False 0 1

Table3

Conditionalprobabilitytableforthenode[3]SleanedontherailinginFig.1.

Propositions Hp Hd

Sleanedontherailing:

True 0 1

False 1 0

Table4

Conditionalprobabilitytableforthenode[4]FingermarksSthroughclimbingin Fig.1.

Sclimbedthebalcony True False

Fingermarksthroughclimbing:

True Pa 0

False 1Pa 1

Table5

Conditionalprobabilitytableforthenode[6]DirectioninFig.1.

Fingermarksthroughclimbing True False

Fingermarksthroughleaning True False True False

Directionofﬁngermarks:

Inwards * Pc1 Pd1 *

Outwards * 1Pc1 1Pd1 *

(*)denotesthefactthattheseprobabilitiesrepresentsituationswhichwillnot occurbecausetheactivitiesclimbingandleaningaremutuallyexclusiveinour example, and the network is not constructed to evaluate the absence of ﬁngermarks.

(7)

ﬁngermarksthroughleaningisfalse,theprobabilitytoﬁndinward

pointingﬁngermarksisdenotedbyPc1.

3.1.6.Node[7]location

SimilartothenodeDirection,thenodeLocationisdependenton

thenodesFingermarksthroughclimbingandFingermarksthrough

leaning,asshownbythearrowsbetweenthesenodesandthenode

LocationinFig.1.Inourcaseexample,weassumethatthereisno

directdependencybetweenthevariableLocationandthevariable

Direction.Theprobabilitytoﬁndtheﬁngermarksonaparticular

locationontherailingdoesnotdirectlydependonwhetherthe

ﬁngermarksareplacedinwardsoroutwardsandviceversa;they

bothdirectlydependontheactivitythatiscarriedout.

Fig. 2 shows the top view of the balcony. During the

investigation,itwasdeterminedthattheonlywaytoclimbthe

balconyisviathedrainpipelocatedontheleftsideofthebalcony.

ForthestatesofthenodeLocation,wedecidedtodividetherailing

intofourareas:theleftbeam,themiddle/leftbeam(withplanter),

themiddle/right beamandthe rightbeam,asshown in Fig.3.

Again,therearemanywaystochoosethepossiblestates.Forthis

scenario,we considerdividingthe railinginto thesefourareas

appropriategiventhestructureandsetupofthebalcony.Theleft

sideisscreenedoffbythedoorwhenopen,theplantershieldsthe

railingandthefoursurfaceareasareapproximatelyequal.

TheprobabilitytableforthenodeLocationisshowninTable6.

Sincetherearefourpossiblestates,wedenotedtheprobabilitiesof

thestatesleft,left/middle,right/middleandrightincase

Finger-marksthroughclimbingistrueandFingermarksthroughleaningis

falsewithPe1;Pe2;Pe3and1 Pe1þPe2þPe3

.Theprobabilitiesin

caseFingermarksthroughclimbingisfalseandFingermarksthrough

leaningistruearedenotedwithPf1;Pf2;Pf3and1ðPf1þPf2þPf3Þ.

3.1.7.Node[8]areaoffrictionridgeskinwithsub-nodes[9]which

hand,[10]palm,[11]ﬁngersand[12]thumb

Giventhatitisknownthatthesuspectlefttheﬁngermarkson

the railing, the corresponding area of the hand that left the

ﬁngermarkscanbedetermined.ThenodeAreaoffrictionridgeskin

withitssub-nodesWhichhand,Palm,FingersandThumbareusedto

incorporatethevariableareaof frictionridge skinthat leftthe

ﬁngermarks,asdiscussedinSection2.2.4.

Inourcaseexample,wechosetodividethehandthatleftthe

ﬁngermark(s)inthreeareas:thepalm,theﬁngersandthethumb.

WithinthenodesPalm,FingersandThumb,thepartofthehandthat

leftthemarkscanbespeciﬁed.Eachnodehastwopossiblestates:

‘true’and‘false’.Whetherthemarkscamefromtherightorleft

hand can be speciﬁed within the node Which hand, also with

possiblestates‘true’and‘false’.Allthesenodesareconnectedto

thesummarynodeAreaoffrictionridgeskin,thatcombinesallthe

information provided in the previous nodes. In this node, the

probabilityofallpossiblecombinationsofthestatesofthenodes

Whichhand,Palm,FingersandThumbissummarized.

In some cases, differentiation between each ﬁnger or even

betweenspeciﬁcareasonthehandmaybemoreappropriatesince

theprobabilityofoccurrenceofcertainareasmaydifferbetween

theallegedactivities.Adirectresultofdeﬁningsmallerareason

thehandisthatthenumberofstatesforthenodeAreaoffriction

ridgeskin increasessubstantially,sinceeach combinationofthe

speciﬁedareasforeachhandshouldbeassignedaprobability.For

example, dividingthehand intosix regions (ﬁveﬁngers and a

palm)andaccountingforthepossibilitythattheleftortheright

hand is used, already results in 126 combinations. Assigning

probabilities toall theseseparate combinations may becomea

difﬁcult task. Since in our case example, we expected the

probabilities to observe _fingermarks of a speci_fic _finger to

differentiatebetweenclimbingandleaning,wechoosethethree

states‘palm’,‘ﬁngers’and‘thumb’.Table7showstheprobability

tableforthenodeAreaoffrictionridgeskin.Fromthistable,wecan

observethatadifferentiationof3areasofthehandresultsin14

possiblestatestowhichprobabilitieshavetobeassigned,varying

from the probability to observe only the left-hand palm, to

observingthecombinationoftheright-hands’ﬁngers,palmand

thumb.Wedidnottakeintoaccountcombinationsoftherightand

the left hand, since we limited our network to one grip of

ﬁngermarksforwhichitisassumedtheﬁngermarksaredeposited

byonehand.

3.2.Caseexample2:Actorthatcarriedouttheactivitydisputed

Considerthesamescenarioasdescribedincaseexample1,but

insteadofclaimingthattheclimbingdidnottakeplace,thesuspect

claimsthatsomeoneelsemusthaveclimbedthebalcony.Hestates

thathevisitedtheapartmentaweekearlieroninvitationbythe

womanandsmokedacigaretteonthebalconywhileleaningonthe

railing.ThewomanconﬁrmstheinformationthatSvisitedaweek

earlier.Thedisputeofthedefenseisnowaimedattheactorofthe

activity[26],resultinginthefollowingactivitylevelpropositions

(deﬁnedassuchinnode[1]PropositionsintheBayesiannetwork

showninFig.4):

Hp:SclimbedthebalconyandSleanedontherailing.

Hd:UclimbedthebalconyandSleanedontherailing.

Thepolicestillfoundonlyonegripofﬁngermarks.However,

this situation is different from case example 1 since if the

ﬁngermark grip belongs toS, the probabilitythat there are no

ﬁngermarksfoundofanunknownindividualhavetobetakeninto

account.ThisresultedintheBayesiannetworkshowninFig.4.

3.2.2.Nodes[2]Uclimbedthebalcony,[3]Sclimbedthebalconyand

[4]Sleanedontherailing

Thepropositionsnowimplythreeactivities,whicharedeﬁned

withthenodesUclimbedthebalcony,SclimbedthebalconyandS

leanedontherailing,eachwithstates‘true’and‘false’.Tables8–10

show the probability tables for these nodes. For example, in

Table8,giventhatHp:SclimbedthebalconyandSleanedonthe

railing istrue,the probabilityforthestate ‘true’of thenodeU

climbedthebalconyis0andtheprobabilityforthestate_‘false’is1.

3.2.3.Nodes[6]FingermarksUthroughclimbing,[7]FingermarksS

throughclimbingand[8]FingermarksSthroughleaning

Thethreedifferentactivitieseachimplyadifferentprocessby

which ﬁngermarksweredeposited andpersistedontherailing,

representedbythenodesFingermarksUthroughclimbing,

Finger-marksSthroughclimbingandFingermarksSthroughleaning.These

nodeshavethestates‘true’and‘false’andtheirprobabilitytables

Fig.2. Topviewofthebalconyinscenario1.

Fig.3.Thefourdifferentareasrepresentingthestatesofthenode‘Location’in Fig.1.

(8)

are similar to the probability table for the node Fingermarks

throughclimbingincaseexample1,showninTable4.

3.2.4.Node[5]backgroundﬁngermarksU

Incaseexample2,thereisanothermechanism possiblethat

needsto beconsidered: ﬁngermarks of one or more unknown

personscouldalreadyhavebeenpresentpriortotheactivitiesthat

havetaken place.Thisis denoted bytherootnodeBackground

ﬁngermarksU,denotedbythecolourgreyinFig.4,withstates‘true’

and ‘false’. Within this node, we consider the probability of

observing backgroundﬁngermarks ontherailingthat arenota

resultofthedisputedactivities.Incasenounknownﬁngermarks

werefoundbesidestheﬁngermarksofS,thebackgroundnodewill

beinstate‘false’withaprobabilityp=1.

3.2.5.Nodes[9]marksofUpresentand[10]marksofSpresent

Thissectionstillfocusesononegripofﬁngermarksdeposited

duringone handplacement,there areonlytwo optionsforthe

Table6

Conditionalprobabilitytableforthenode[7]LocationinFig.1.

Locationofﬁngermarks:

Left * Pe1 Pf1 *

Middle/left * Pe2 Pf2 *

Middle/right * Pe3 Pf3 *

Right * 1ðPe1þPe2þPe3Þ 1ðPf1þPf2þPf3Þ *

(*)denotesthefactthattheseprobabilitiesrepresentsituationswhichwillnotoccurbecausetheactivitiesclimbingandleaningaremutuallyexclusiveinourexample,and thenetworkisnotconstructedtoevaluatetheabsenceofﬁngermarks.

Table7

Conditionalprobabilitytableforthenode[8]AreaoffrictionridgeskininFig.1.

Areaoffrictionridgeskin:

Left–Palm * Pg1 Ph1 *

Left–Fingers * Pg2 Ph2 *

Left–Thumb * Pg3 Ph3 *

Left–Palm–Fingers * Pg4 Ph4 *

Left–Palm–Thumb * Pg5 Ph5 *

Left–Fingers–Thumb * Pg6 Ph6 *

Left–Palm–Fingers-Thumb * Pg7 Ph7 *

Right–Palm * Pg8 Ph8 *

Right–Fingers * Pg9 Ph9 *

Right–Thumb * Pg10 Ph10 *

Right–Palm–Fingers * Pg11 Ph11 *

Right–Palm–Thumb * Pg12 Ph12 *

Right–Fingers–Thumb * Pg13 Ph13 *

Right–Palm–Fingers-Thumb * 1ðPg1þþPg13Þ 1ðPh1þþPh13Þ *

(9)

sourceoftheﬁngermarks:theﬁngermarksarefromanunknown

personUorthe_ﬁngermarksarefromS,denotedbythe_ﬁndings

nodesMarksofUpresentandMarksofSpresent.Bothnodeshave

states‘true’and‘false’.Thearrowbetweenthesenodesrepresents

thedependencybetweenthem:ifMarksofSpresentistrue,Marks

ofUpresentcannotbetrue.

TheprobabilitytablesforthenodesMarksofSpresentandMarks

ofUpresentareshowninTables11and12.ThenodeMarksofS

presentdependsonthetwonodesFingermarksSthroughclimbing

andFingermarksSthroughleaning.Table11showsthatifoneof

thesenodesisinstate‘true’,theprobabilitythattherearemarksof

Spresentis1.Ifbothofthesenodesareinstate‘false’,thereisa

probabilityof0thattherearemarksofSpresent.ThenodeMarksof

Upresentdependsonthreenodes:FingermarksUthroughclimbing,

BackgroundﬁngermarksUandMarksofSpresent.Table12shows

thatifthenodeMarksofSpresentistrue,theprobabilitythatthere

aremarksofUpresentisfalse.Thisisbecausewefocusononegrip

ofﬁngermarksleftduringoneplacement.

3.2.6.Findingnodes[11]to[17]

ThenodesDirection,Location,andAreaoffrictionridgeskinare

deﬁnedthesamewayasdescribedinpreviousSections3.1.5–3.1.7,

withanadditionalarrowfromthenodesBackgroundﬁngermarksU

andFingermarksUthroughclimbing.ThenodesWhichhand,Palm,

FingersandThumbaredeﬁnedexactlythesamewayasdescribedin

Section 3.1.7.An exampleof the probabilitytable for thenode

DirectioninFig.4isshowninTable13.

3.3.Caseexample3:Multiplegrips

Oftenthereismorethanonegripofﬁngermarksfoundonan

item. Supposethat in additiontothe ﬁrstgrip, anothergripis

foundontherailing.Again,thesuspectclaimsthathevisitedthe

apartmentaweekearlierandleanedontherailingofthebalcony

and this information is again conﬁrmed by the woman. The

propositionsbroughtforwardbytheprosecutionandthedefense

arethesameasusedforcaseexample2:

Hp:SclimbedthebalconyandSleanedontherailing.

Hd:UclimbedthebalconyandSleanedontherailing.

Now the Bayesian network should account for two grips,

resultingintheBayesiannetworkshowninFig.5.

3.3.2.Structureofthenetwork

TheBayesiannetworkinFig.5consistsoffour‘modules’.The

networkstartswithapropositionnodePropositions[1],followed

by the nodes describing the alleged activities: U climbed the

balcony,[3]Sclimbedthebalconyand[4]Sleanedontherailing.

Table9

Conditionalprobabilitytableforthenode[3]SclimbedtherailinginFig.4.

Propositions Hp Hd

Sclimbedthebalcony:

True 1 0

False 0 1

Table10

Conditionalprobabilitytableforthenode[4]SleanedontherailinginFig.4.

Propositions Hp Hd

Sleanedontherailing:

True 1 1

False 0 0

Table11

Conditionalprobabilitytableforthenode[10]MarksofSpresentinFig.4.

FingermarksSthroughclimbing True False

FingermarksSthroughleaning True False True False

MarksofSpresent:

True 1 1 1 0

False 0 0 0 1

Table8

Conditionalprobabilitytableforthenode[2]UclimbedtherailinginFig.4.

Propositions Hp Hd

Uclimbedthebalcony:

True 0 1

False 1 0

Table12

Conditionalprobabilitytableforthenode[9]MarksofUpresentinFig.4.

FingermarkUthroughclimbing True False

BackgroundﬁngermarksU True False True False

MarksofSpresent True False True False True False True False

MarksofUpresent

True * * * 1 * 1 0 0

False * * * 0 * 0 1 1

Table13

Conditionalprobabilitytableforthenode[11]DirectioninFig.4.

BackgroundﬁngermarksU True False

FMUthroughclimbing True False True False

FMSthroughclimbing True False True False True False True False

FMSthroughleaning True False True False True False True False True False True False True False True False

Direction:

Inwards * * * * * * * Pi1 * * * Pj1 * Pk1 Pl2 *

Outwards * * * * * * * 1Pi1 * * * 1Pj1 * 1Pk1 1Pl2 *

(10)

(11)

Thesenodeshavethesamesetupasincaseexample2.Belowthese

nodesaretwonearlyidenticalmodulesthatrepresenttwodistinct

fingermarkgrips.Thefirstgripoffingermarksisdescribedbythe

nodesontheleft-handsideofthenetwork,indicatedby(1).The

secondgripofﬁngermarksisdescribedbythenodesindicatedby

(2).Betweenthesetwosub-networksisamoduleconsistingoffour

greennodesthatdescribedependenciesbetweenthetwotraces.

Weconsider conditional dependenciesbetweenthe two traces

basedonthelocationofthemarks,thedirectionofthemarksand

whetherornotthetwomarkswereleftbythesamehandsincethe

ﬁndingsmaybedependentonthesefactors.Weconsiderthem

conditionallyindependentfromthepropositions.Wechosethese

dependenciessince weconsiderthattheprobabilityofthetwo

marksbeingfromthesamedonorishigherwhentheyarefoundat

thesame location,havethesamedirectionandare leftbytwo

differenthands,thanifeitherlocationordirectiondiffer(where

locations within reach of both arms still have an increased

probabilityfortheﬁngermarksbeingfromthesamesource).

Ifthetwogripsaredepositedduringthesameactivity(holding

therailingwithbothhandswhileclimbingorleaningontherail

withbothhands),therearetwooptionalsituations:thedeposition

ofthetwomarksisstrictlyconstrainedintime,e.g.theymusthave

beenplacedattheexactsamemomentduringthesameactivityor

thedepositionof thetwomarksislessconstrained intimeand

multipleinteractionsbetweenhands and therailingtook place

duringthesameactivity.Tobothsituations,itappliesthatifthe

two ﬁngermark grips are found in close proximity, this will

inﬂuence the probability that they were left by the same

individual,regardlessoftheactivitiesdeﬁnedinthepropositions

thatledtotheirdeposition.

Ifweassumethetwomarksarestrictlyconstrainedintimeand

wereleftthroughthesameactivity,giventhecasecircumstances,

thereisahighprobabilitythattheywillhavethesamedirection,

sinceitisunlikelytoplaceonehandinwardsandonehandoutwards

whencarryingoutthesameactivityinthesamemomentintime.

Furthermore,ifthetwomarkswereleftthroughthesameactivityat

thesametime,theycannothavebeenleftbythesamehand.

However,sinceboththeactivitiesleaningandclimbingarea

dynamic process, it is unlikely that this assumption holds. If

multipleinteractionsbetweenhandsandrailingmayhavetaken

place,itisnotunlikelytoﬁndmultiplemarksofthesamehand

closetogether.Also,dependingonhowstrictorbroadtheactivities

aredeﬁnedindynamicsandtime,itmaybeconsideredequally

probabletoﬁndthemarkshavingthesamedirectionoradifferent

direction.Withaverybroaddeﬁnitionandmultipleinteractions

withtherailingover extendedperiodsof time,only locationis

expectedtobeadependentfactorbetweenthetwomarks.

We haveaddedfournodesto thenetworkthatmodel these

dependencies.Node [31] Same direction? models whether both

markshavethesamedirectionornot(respectivelystate‘true’or

‘false’),andisdependentofthedirectionnodesforthetwoseparate

grips.Ifthedirectionofbothgripsisequal,thenodeSamedirection?

isinstatetruewithaprobabilityp=1.Otherwise,thenodeSame

direction?isinstatefalsewithaprobabilityp=1.Node[32]Same

location?modelswhetherbothmarkshavethesamelocation.The

statesofthisnodeconsistofallpossiblecombinationsofthestates

forthenodesLocation(1)andLocation(2),whichresultsin ten

combinations.IfLocation(1)isleftandLocation(2)isleft,thenode

Same location? is in state ‘left-left’ with a probability of p=1

Choosingfortwopossiblestates‘true’and‘false’isalsoapossibility.

However,in this case theproximityof two consecutive beams

cannotbetakenintoaccountin thenode[34]Samesource.The

dependencybetweentwohandsismodelledwithintheNode[33]

Samehand?,withstates‘true’and‘false’.IfWhichhand(1)andWhich

hand(2)arebothleft,thenodeSamehand?istruewithaprobability

ofp=1Thenode[34]Samesource?containsaprobabilitytablethat

holdstheprobabilitiesfortheﬁngermarksbeingfromthesame

donorbasedontheirrespectivelocations,directionandleftorright

handsetting.Additionally,node[23]MarksofSpresent(2)isnow

dependentonthenode[34]Samesourceandnode[20]MarksofS

present(1)(inadditiontonodes[11]and[12]).

Thisnetworkcouldbeextendedtoanetworkthatallowsforthe

evaluationofmorethantwogripsofﬁngermarks,byconcatenating

multiplesub-networksinthesameway.Whenconstructingsucha

network,possiblenewdependenciesbetweenvariablesdescribing

different grips should be considered. A combined network

accounting for multiple gripsmakes a complete analysis of all

the_ﬁngermarkspresentonanitempossible.

4.Discussionandconclusion

Inthispaper,wehavedescribedaframeworkfortheevaluation

of ﬁngermarksgivenactivitylevel propositionswiththe useof

Bayesiannetworks.Weprovidedanoverviewofthecurrentstate

of knowledge of the variables that provide information on

ﬁngermarks given activity level propositions, followed by an

implementationof thesevariables ina Bayesian network using

threecaseexamples.Theresultingnetworksenablesthe

evalua-tion of (multiple) ﬁngermark grips present on an item given

propositionsthatdisputetheactivitythatwascarriedoutorgiven

propositionsthatdisputetheactorthatcarriedouttheactivity.

TheBayesiannetworksproposedinthispapercouldfunctionas

basic networks for the evaluation of ﬁngermarks, with the

possibilitytobemodiﬁedaccordingtospeciﬁccasecircumstances.

Furthermore,partsofthenetworkmayfunctionasbuildingblocks

tocreatenewnetworksforitemsotherthanabalconyrailing,to

evaluate ﬁngermark grips given activity level propositions.

AnotheradvantageofusingofBayesiannetworksisthatitmakes

theprocessofevaluationoftheﬁndingsexplicit.Thenetworkcan

beusedasatooltodiscusstheselectedvariables,thedependencies

between them and the probabilities used, resulting in open

discussionsincourt.

Theprinciplesdiscussedinthispaperaremeanttobeusedasa

guidelinetohelpforensicscientistsmakewell-consideredchoices

dependingonthecaseathand.Theproposedlistofvariablesisa

recommendation: it depends onthe case circumstances which

variablesmaybeimportanttoconsider.Thechoiceofthestatesof

the variables also depends on the case circumstances, the

possibilities toobjectively measurethe possible states and the

feasibility of assigningprobabilities tothestates. These factors

needtobecarefullyconsideredwhenselectingthestatesofthe

nodes.Similarly,weproposeddependenciesbetweenthevariables

basedonourcaseexample,whichshouldbereconsideredwhen

applyingtheframeworktoadifferentcaseexample.

The ﬁnal step to complete a Bayesian network is to assign

probabilitiestothenodes[28].AccordingtoTaylor,Kokshoornand

Biedermann[29],aforensicscientisthasanumberofoptionstodo

this(mentionedinorderofpreference):performexperimentsby

simulating the case circumstances, use values reported in

literature from studies using similar case circumstances and

outline the differences when reporting, consider a range of

reasonable values and examine the sensitivity of the LR (see

[30]),assignvaluesbasedontheexpert'sexperienceorknowledge,

or not carry out an evaluation. For ﬁngermarks, the current

situationisthatevaluationsofﬁngermarksgivenactivitylevelare

notcarriedoutbyforensicexperts.Thisleavestheevaluationof

ﬁngermarks given activity level propositions up to the court

although the forensic scientist has the specialized knowledge

regarding the variables that is required to properly assign

probabilities[29].

Intheﬁeldofforensicbiology,anincreasingbodyofliteratureis

(12)

transfer,persistenceandrecoveryofDNAinrelationtoactivities

(seefor example[31,32]).These studiesinvolve experimentsin

whichparticipantscarriedoutactivitiesthatresultedintouching

surfacesoritems,andfactorsliketransferandpersistencewere

evaluated in relation tothe activities performed. The study of

ﬁngermarks in time and space would beneﬁt from similar

experimentaldesigns.Experimentsintoprobabilitiesoftransfer,

persistence,recovery,direction,locationofﬁngermarks,orwhat

ﬁngers are used when carrying out different activities with a

particularitemwouldhelpforensicscientiststoassign

probabili-tiestothesevariablesincases withsimilarcase circumstances.

Althoughtheobtainedprobabilitiesmaynotalways bedirectly

applicable to other cases, the experimental data may still

contributetoascientiﬁcknowledgebase[29]andmaycontribute

toabetterunderstandingofthegeneralmechanismsofﬁngermark

dynamics.

Other recommendations for further research are designing

methodstoobjectivelymeasureaspeciﬁcvariable.Forexample,

thereisnomethodavailabletoobjectivelymeasurethedirectionof

aﬁngermarkonasurface.Anotherexampleisthevariabletransfer:

howdowemeasurethetransferofaﬁngermarktoasurfaceasa

resultofanactivity? Nowadays,ﬁngermarkscanbescored(for

example by the CAST scale [14]) to compare the quality for

individualizationpurposes.However,thequantityofﬁngermarks

transferredtoasurfacemayalsoprovideinformationonactivity

level. These examples show that for some variables describing

ﬁngermarks at activity level, a clear deﬁnition or method to

measure the variable is required before the variables can be

describedbycasespeciﬁcexperiments.

Withthis paper, we want toinitiate a discussionabout the

evaluationofﬁngermarksgivenactivitylevelpropositions.Until

now,thistopichasbarelybeentouchedupon,possiblybecausethe

necessityisnotacknowledged.However,anevaluationof

ﬁnger-marksgivensourcelevelpropositionsdoesnotalwaysamountto

theactivity[9].Inthesecases,anevaluationoftheﬁngermarks

givenactivitylevelpropositionscouldaffectthestrengthof the

evidencewithinthecasecircumstances.Wehopethispaperwill

leadtonewperspectivesonthistopicandstimulatesopportunities

forfurtherresearch.

Author'scontribution

Anouk deRonde: Conceptualization,Methodology, Software,

Formalanalysis,Investigation,Writing–Originaldraft,Writing–

Review and Editing, Visualization, Project administration. Bas

Kokshoorn: Conceptualization, Methodology, Software, Formal

analysis,Writing–ReviewandEditing,Visualization.Christianne

dePoot:Conceptualization, Methodology,Writing–Reviewand

Editing, Supervision, Funding Acquisition. Marcel de Puit:

Conceptualization, Methodology,Writing – Reviewand Editing,

Supervision,FundingAcquisition.

Conﬂictsofinterest

Nonedeclared.

Funding

This work was supportedby the RAAK-PRO funding of the

Foundation Innovation Alliance (SIA – Stichting Innovatie

Alliantie),researchgrantno.2014-01-124PRO.

Acknowledgment

WewouldliketothankCarolineGibbforhercommentsonan

earlierversionofthismanuscript.

AppendixA.Supplementarydata

Supplementary data associated with this article can be found, in the

onlineversion,athttps://doi.org/10.1016/j.forsciint.2019.109904.

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