ForensicBiometrics:

From twocommunities to OneDiscipline

Didier Meuwly*, Raymond Veldhuis**

*Netherlands Forensic Institute, WISK, 2490AAThe Hague,The Netherlands **The University of Twente,SAS,7500AEEnschede, TheNetherlands *[email protected] **[email protected]

Abstract: This articledescribes how thefieldsofbiometricsand can contributeand benefitfromeachother.The aimistofosterthe developmentof newmethods andtoolsimproving thecurrent forensic biometricapplications and allowing forthe creation of newones. Thearticle begins with adefinitionand a summary of thedevelopmentinforensicbiometrics. Then it describesthe data and biometricmodalities of interest in forensic scienceand theforensicapplications embedding biometrictechnology. On this basisitdescribes thesolutions and limitations of thecurrent practice regardingthe data,the technology andthe inferencemodels.Finally,itproposes research orientations forthe improvement of thecurrent forensic biometricapplications andsuggestssomeideas forthe developmentofsomenew forensic biometricapplications

1Introduction

Forensic scienceisdefined as thebody of scientific knowledge andtechnicalmethods used to analyseand interprettraces,inorder to answer questions relatedtocriminal, civil andadministrativelaw.Itfocuses in particular on thedemonstrationofthe existenceand theinvestigationofanoffence, on theindividualization of aperpetrator andonthe descriptionofamodus operandi.The practice of forensic scienceisfoundedon4basic inferences:identification, individualization, associationand reconstruction [IR98].These inferences arestructuredin3levels: thesource level, theactivitylevel andthe offence level[Co98].The source levelfocuses on thequestionofthe origin of atrace,the activity levelconcentrates on theactivitythatleads to atrace andthe offencelevel adresses thequestionifanactivityisconstitutiveofanoffence.

Biometricsisthe setofautomated methods used forthe recognition of human beings, measuringand analyzingstatistically theirdistinctivephysical andbehavioural traits. Themethod consists of theextractionand comparison of biometricfeaturesfroma referenceand atestsample, followedbythe computationofascorerepresentinga distance or asimilaritybetween thetwo samples[Wh10].

Currently scores areusedin3typesofforensicinferencesatsource level: identification andidentityverification, individualization, andassociation. Identificationand identity

7 verificationare decisions about theidentityofaperson. Individualization is adescription of theevidentialvalue of atrace,inthe light of apairofmutuallyexclusive hypotheses relatedtothe source of this trace. Associationconsists of linking andselecting objects, peopleand events.Moreconcretely biometrictechnology playsarole in severalforensic applications:the identitymanagementand theidentityverificationinthe criminal justice chain, theidentificationofmissing persons fromamass disaster,the forensic investigationand intelligenceaswellasthe forensic evaluation of biometricevidencein court. Together theseapplications form thefield of forensic .

Alot of biometricsolutions areimplemented in theforensicpractice, oftenasthe result of arequest of thelaw enforcementagenciestowards industrialand academic partners. Butthe fieldstill faces severe practicallimitations due to an insufficientunderstanding of its contextand needsfor an optimal implementation of toolsand methods.Even comprehensivedocuments on biometrics addressing theforensicaspectdonot usethe forensic inferencemodelstodescribethe challengesand opportunities in forensic biometrics [Wh10].Acause maybefound in theimmaturity of theforensicresearch culture [Mn11] andinthe rarity of theliteratureaddressing this specific topic[DC08].

Theaim of this articleistodescribehow thefieldsofbiometricsand forensic science can contribute andbenefit fromeachother,inorder to foster thedevelopmentofnew methods andtools improving thecurrent forensic biometricapplications andallowing forthe creation of newones.

2Development

Methods like forensic anthropometry [Be86],forensicdactyloscopy [Ga92] andle portraitparlé [Re05] existfromthe endofthe 19thcentury.Theyexploitphysical and behavioural traits forthe individualization of perpetrators of criminal infringements. From the1960’sthe developmentand implementation of automaticfingerprint identificationsystem(AFIS)constitutesthe firstforensicbiometric application: the automation of theidentityverificationonbasis of tenprint cards [BS01].Inthe 1980’s thediscovery of forensic DNA profilingled to thedevelopmentand implementation of similartools andapplications:the identity verificationonbasis of DNAreference material usingacomputerized DNA ,the selectionofsubsetsofindividuals and theindividualization of persons frombiologicaltraces.

In the1990’sspeaker,faceand gait recognitionbecameofinterestfor forensic biometrics,asaconsequenceofthe developmentofmobile telecommunication and camerasurveillancetechnologies (CCTV). During thesamedecadethe firstsolutions combiningbiometric technologies andthe Bayesian likelihood ratio inferencemodel were proposed forevidenceevaluation[CM00].

After 2001 theinterestrosefor soft biometricmodalitiessuchasbody measurements (height,width,weight)and proportions,gender, hair,skincolour andclothing characteristics. This interest wasmainlymotivated by thepossibility of capturing these features in unconstraint environments.However,the limiteddistinctivenessand

permanence of thesefeaturesenhanced thenecessity to consider amultimodalapproach [JDN04].

In thecurrent decadethe developmentofnew biometricmodalities canbeexpected,for instance modalitiesexploiting spectroscopicpropertiesofthe biologicaltissues outside of thevisible rangeorbeing spin-offs of theunprecedenteddevelopmentofmobile communicationtechnology. Theirpotential forforensicbiometricswillbeinvestigated.

3Dataand modalities

In theforensiccontext areference sample is sometimesnamed controlmaterialor knownitem, when atestsamplecollected on acrime sceneisoften denoted as crime scenesample, tracematerial, questionedorunknownitem. Case relatedbiometric data arethe referenceand test samplescollected andusedfor casework purposebylaw enforcementagencies. They arealsoofgreat interest forforensicbiometric research.

Some biometrictracesand marksare capturedphysically (biologicaltraces, fingermarks, earmarks, bitemarks, lipmarks), otherdigitally (face,voice,body measurements,gait). Some attributes closelyrelated to thehuman body like clothing andfootwear areoften treatedasbiometric modalities in forensic science, becausetheyare collected, analyzed andinterpreted in thesameway as biometrictraces andexploitedusing thesame inferencemodels.The stability of amodality overtimedeterminesthe obsolescence of thecaserelated data forinvestigation, fromlifetimefor fingerprintand DNA, to some months or yearsfor face andspeaker recognition.

In ordertobeofforensicinterest, thebiometric modality hastobeavailable as atrace andneedstobedistinctive. On acrime-scene,fingermarks andbiological traces are searched in priority because they areoften availableand canbeverydistinctive. On the otherhandthe iris pattern, even if very distinctive, is onlyveryrarelyavailable as a digitaltrace.Digital traces mayembed informationabout thebody lengthofa perpetrator, but this modality is onlyprivilegedifnoother optionisavailable,due to the its poor distinctiveness[Al08].The modality also needstobestableand robusttothe forensic conditions.Facerecognition is commonlyexploitedfor forensic investigation, but suffers fromseverelimitations.The facial features canchange significantly over even shortperiods of time andthe unconstrainedvideo capturesconstitutingthe main part of thetrace material canlooseasubstantialpartofthe distinctivenessofthe modality.

Theoverall performance of abiometric technology is largelyinfluencedbythe quality of theinput data conditionedbythe acquisition andenvironmentalconditions.These factorsare commoninall biometricdeployments,but forensic processestendto maximize theirvariability [DC08].

9 4Applications

This sectiondescribes theforensicbiometric applications anddetails theroleof biometrictechnology in each of them.Inpreambleithas to be stressed that thereliability of anyforensicbiometric applicationreliesonthe integrityofthe identitymanagement used by thecriminaljustice chain[Me10].The identity infrastructureinplace needsto able to create,challenge andend biometricidentitiesreliably. Forexample,beforethe useofbiometric solutions in theDutch prisons,someindividualswereserving sentences, substituting themselves to theconvicted criminals. Nowadays,the Netherlands have implementedasystem combiningthe andface modalities to identify and verify,incaseofserious crime, that thepersonbehindaclaimedidentity remainsthe same along thewholecriminaljustice chain, fromthe arrest to thedetention to servethe sentence [PG11].

4.1Forensicidentificationofmissing persons

Theidentificationofmissing persons fromamass disaster depends on theformofthis disaster,closedoropen. Acloseddisasterrelates to aknownnumberofindividuals from adefined group, likeanaircraftcrash with apassengerlist. Open disastersliketraffic accidents,natural disasters, technicalaccidents (fires,explosions), terrorist attacksand events occurring within thecontextofwar relatestoanunknownnumberofindividuals fromanundefinedgroup. Combinations of thesetwo formsare also conceivable(e.g. aircraft crashinaresidentialarea) [In09].Whenthe priorprobabilities can be assigned, theevidentialvalue of thebiometric features can be assessedand thedecisionthresholds can be determined.Closed-set identification(1toN)and open-setidentification(1to N+1) frameworksapply,respectively, to thesetwo typesofdisaster. When theprior probabilities cannot be assigned andthe decision thresholds cannot be determined,the likelihood ratioinference modelappliestoassessthe evidentialvalue of thebiometric features [Bu11, BTM12].

4.2Forensicinvestigation

Biometrictechnology contributestoforensicinvestigationinassociatingtracesto persons presentinadatabase,producingranklists andselecting subsetsofpersons from whichthe tracemay originate. Forinstanceanautomatic fingerprint identification system or acomputerized forensic DNA database areusedfor comparingatracetothe N individuals of adatabaseand selectingthe Mindividuals most similartothe trace (closed-setselection, MfromN). In asecond phase forensic examinersrefinethe results of theautomatic selectionexcluding some more referencesamples,based on criteria that arenot addressedbythese methods at present. Theimportanceofthe human-based phase increaseswiththe complexity of thetrace,e.g.superimposed fingermarks or biological traces containing partialDNA profilesfrommorethanone contributor.Thiscombined combined approach (automated andhuman-based)can be describedasanopen-set selection(MfromN+1).

1 4.3Forensicintelligence

Biometric technology is used forforensicintelligence to associatetracesfromdifferent cases,producingranklists andselectingsubsetsofcases with traces that maybefromthe same origin.Onlycomparing tracematerialisthe most challenging applicationfromthe point of view of biometrics [RWM06].For instance theinformation system (IS)of Europol will integrateinthe near future forensic intelligence capabilities forthe DNA, fingerprint andfacemodalities [Eu11].

4.4Forensicevaluation

Theevaluationofbiometric evidence in courtconsists of applying thebiometric technology forforensicindividualization. Thescore computed is considered as forensic evidence (E)and theevidentialvalue of E is assessed in thelight of apairofmutually exclusivehypothesesaboutthe origin of thetrace material.Generally thefirst hypothesis (Hp)issupportedbythe prosecution andstatesthatthe tracematerialoriginatesfromthe suspectedperson. Thesecond hypothesis(Hd)issupportedbythe defenceand states that thetrace material originates fromanotherindivual,randomly chosen within therelevant populationofpotential sourcesofthe trace. Theevidentialvalue is calculated as theratio of twoprobabilities: theprobability of theevidencewhenthe prosecution hypothesisis true dividedbythe probability of theevidencewhenthe defencehypothesisistrue. I represents therelevantbackground informationabout thecase, forinstancethe selection processofthe suspectedpersonand thenatureofthe relevant population[DC08]. The result is expressedasalikelihood ratio, calculated as follows:

Pr H E, I Pr H , I ()p Pr ()EHp,I ()p = ⋅ Pr H E, I Pr H , I ()d Pr ()EHd,I ()d Posterior Likelihood Prior probabilityratio ratio probabilityratio

Theposterior probability ratioiscalculatedasthe multiplication of theprior probability ratiobythe likelihood ratio.The role of theforensicpractitionerislimited to the assessmentofthe likelihood ratio.Toprovide theprior probability ratio andtomake decisions on basisofthe posterior probability ratioisthe dutyofthe court. This approach is considered as logicaland balanced [Ev98] andthe LR can be seen as the metric describing theevidentialvalue [Go91]. Abiometric LR-based system is asoftwaresystemthatcombinesthe useofbiometric , technologies andthe likelihood ratioapproach to assess statistically the evidential valueofabiometrictrace associated to areference sample.The quality of the inferencestronglydepends on thequantity andpropertiesofthe data used to estimate the within andbetween-source variability [Me06].Suchanautomatic approach complementsthe human-based approach usingknowledgeand experience to assign personalprobabilities. ThestrengthofaLR-based system is to provide statistical probabilities on theset of distinctivefeaturesthatcan be extractedautomatically.The strengthofhuman beings is to also consider features that cannot be handled yetbythe biometrictechnology, likethe thirdlevel detailsinfingermarks or sociolinguistic aspects

11 of speech.Statisticalprobabilities areconsidered as more objective andpersonal probabilities more subjective. Theclassical “forensicidentification” disciplinesrelying mainly on personal probabilities forthe assessmentofthe evidence arebeing increasinglychallenged [SK05],especiallybecause of thedevelopmentofevidencebased on DNA profiles governed by statisticaldataand theevolvingrequirementsfor theadmissibility of evidence following theDaubert decision by theSupremeCourt of theUSA [DC08].The LR approach is considered as promisinginforensicbiometrics. It hasbeenfirstly implementedfor theDNA modality [Ev98],followedbyLR-based systemsdeveloped forthe speaker recognition modality[GR06] andmorerecently forthe fingerprint modality [NES12].Inthisrespect,forensicbiometricscan be considered as aforerunner in the“forensic identification scienceparadigmshift”[SK05].

5Improving thecurrentapplications

5.1Modalities

Despitethe widely spreaded usageofthe biometrictechnologieswithinforensicscience, some biometricmodalities have escapedtocatch theattentionofthe biometric community,probablydue to thefactthattheyare only exploitedfor specific forensic purposes.For instance thesizeand shapeofhardtissues (softbones, bonesand teeth) andthe results of dentistryand surgeryonthese tissues areexploitedbyforensic anthropologists, mainly for post-mortem identification. Thesefeaturesare considered as very distinctive, but more systematic statisticalresearch is desirabletobeabletoassess theirevidentialvalue.Together with fingermarks andbiologicaltraces,earmarksand footwear marksare collectedinhighvolumecrime andworkablefor forensic investigation, intelligenceand individualization purpose. Butcontrarily to the fingermarkand DNA modalities,noanalytical modelisavailable yettodescribethe distinctivefeaturespresent in ear andfootwear marks. They stillrepresent achallenge forpattern recognition andstatistics, limiting de facto thepossibility to build forensic biometricsystems basedonthese modalities [RWM06].

5.2Technology

Biometric featureextractionand comparison algorithmsare generallyfully automatic andoptimized to minimize processing time.Inthe forensic contextthe need forspeed hasalowerpriorityand semi-automatic featureextractioncan be considered.Specific implementations designedfor thefeature extraction andcomparisonfromforensicdata shouldfocus on theamount of distinctiveinformation usable in theforensicdata, even at thecostofincreasingthe processing time.For instance,the featureextractionand comparison of fingermarks andfingerprintsfocuses on theminutiae(positionand angle), but asystematicuse of theextendedfingerprint featureset (EFS)asdefined in the ANSI/NISTITL-12011 standard mayimprove theperformance [IHK11].Inthe same

1 waythe automatic processing of higher-level speakerdependent features fromspeech samplesofforensicquality maybebeneficial[GR12].

5.3Dataand testing

Biometricdataintrinsically involve some privacyissues, meaningthattheir usefor research impliesauthorizations.Their statuteofrealdataraisesthe question of the ground truthoftheir origin,which is formally unknownfor tracesamples.The amount of caserelated data depends on theforensicprocess.Theymay be collectedinlarge quantitiesand structured in databasesfor forensic investigation. Forforensicevaluation, theamount of data is stronglycasedependent.The requirement in termsofquantity and quality of data depends on whichforensicapplicationthe technologicaldevelopmentis intended.Whenapproachingthe quality of real data,simulated data shouldbeusedin thetrainingphase,because theirproduction is controlled andthe ground truthoftheir origin is known. Thetestphase shouldatleast containsomesetsofrealdata, andthe validationofasystem shouldbeperformed usingmostly real data.Research databases constitutedofcaserelated biometricdataremainunfortunately toorare[Le06, Kr09]. Thelimitationofaccesstorealforensicdatafor research purposeisareality,but improvement is possibleinlinewith thenew EU opendatastrategy“Data is thenew gold”.Awaytoprovide an indirect access to thedatawithout compromising their security andprivacy consists of developing onlineevaluationplatforms.Sucha mechanismallowsfor theevaluationofbiometric systemsusing real forensic data againstappropriate performance metricswithout direct access to thedata. It requires, firstly, to make explicit theforensicbiometric processesand to agreeonthe relevant metricsfor theirevaluation. Secondly, it requiresimplementingthe evaluation mechanisms andsharing thedataand resources. Finally, it requiresacoordination action to feed themostrelevantresults to standardizationbodies,inorder to improve internationalstandardization. TheEUproject “Biometric EvaluationAnd Testing” [Be12],develops such an approach,but not forforensicbiometrics.

5.4Applications

Closed-set(1toN)and open-set(1toN+1)forensicbiometric identificationprocesses areevaluated in standard operationalconditions withstandard errormeasures (false identificationrate/falseacceptanceand falserejection rates) andperformance metrics (CumulativeMatchingCurve-CMC /EqualErrorRate-EER, DetectionErrorTrade-off curve-DET).But in theforensiccontextmoretransparencyisneeded in theway theprior probabilities areassigned, theevidentialvalue fromthe biometricdataisassessedand thethresholdsare determined,globally or personally. Thescalability of thetechnology in theforensicbiometric processesdepends on the modality.But within amodality,italsolargely depends on theprocessand thequality of data involved. TheUSNationalDNA Index(NDIS)containsreference DNAprofiles frommorethanthan107individuals.The performance of this technology is sufficientto implementanidentityverificationapplicationbased on thecomparisonofreference samples, but more performance studies aredesirable forforensicintelligence and investigationdealing with test samplesmimicking thelimited quality of thetraces

13 [Hi10]. Appropriate performance metricsare also needed to characterize selection processes. Therankofatarget as afunctionofthe quality of thetestdatamay be studied usingcostfunctions basedonthe CMC, in orderfor thesizeofthe shortlist(M) not to be fixedbut beingafunctionofthe quality of thedata. Forforensicindividualization, theabsence of underpinning statisticaldatainthe “classic forensic identification disciplines” is viewed as amainpitfall that requiresaparadigm shift[SK05].Outside of theDNA modality,the resultscomputed by LR-based systems usingbiometric technology arerarelyintegratedinthe forensic evaluation. Firstly, no generalmethod is currently describedand availabletoevaluateand calibratethe results of LR-based systems. Agreementexist on theuse of Tippettplots [MD01] andthe measureofthe ratesofmisleadingevidenceinfavourofHp and Hd [Ne06] to measure theperformance of such systemsand on theuse of thecostlog likelihood ratio (Cllr)for theirdiscriminationand calibration. Calibration is ameasure of reliabilityofthe LR value. Theevidentialvalue of calibrated LRs tends to increase when thediscrimination power of the LR-based system increases[RC07].The waytoevaluatesomeother aspects of LR-based systems, like theirrobustness, coherenceand generalisationisstill work in progress. Secondly, thedevelopmentofmethods to combinethe evidential valuecomputed by automaticapproaches andassessedbyhuman-based approaches is still in progress. Technicalsolutions existwithinbiometricstocombine results at different levels (feature, matchscore anddecision),using rule basedapproaches (majority voting, sumrule, productrules), or algorithmsbased on SupportVectorMachine(SVM),fuzzy clustering, radial basisneuralnetworksoreventofuseinformation betweendifferent levels [SVN07].These solutions maybetestedand adopted forsoftbiometric multimodal approaches developedfor forensic investigationand intelligence andfor thefusionofthe modalities used forthe identity verification andidentificationinthe criminal justice chain. Butfor forensic evaluation thereare some particular demands in termsoflogic andtransparencyfor themethodology used to combineresults [AF09].The solution currently explored by theforensiccommunity relies on theuse Bayesian Networks,but despiteproviding logicand transparency to theprocess, its complexity is amajor obstacletoits implementation[Ta06].

5.5Challenges

Thetable 1summarizes aseriesofcurrent challenges forasetofbiometric modalities relevant forthe 4forensicapplications defined supra.The levelofthese challenges depends mainly on theavailability of themodality andonthe maturity of thetechnology; they areatamuch lowerlevel forthe soft biometricmodalities than forthe fingerprint modality.For forensic identificationthe challenge focusesonthe developmentof referencedatabases andontheir management,increasingthe integrity, quality and interoperability of thedata. Forforensicinvestigationand intelligence, thechallenge focusesonthe automationofthe processes, on theimprovement of theperformance for real tracesamples,generally of lowquality,and on thescalabilityofthe technology for largedatabases.For forensic evaluationthe challenge focusesnot only on the developmentofsemi-automaticmethods basedonthe likelihood ratioframework,but also on theintegration of expert-based andsemi-automatic methods into hybrid methods.

14 Forensic Biometricmodality application FingerFaceSpeaker Gait Softmodalities Forensic Improve thedata- Developthe conceptof Notrelevantregarding theweak identification basesintegrity and forensic face/speaker distinctivenessofthese modalities thequality of refe- database rencesamples Improve interope- rability Forensic Improve technolo- Improve technology scalability Evaluate the investigation gy scalability (da- Diminish theconstraintsonthe trace possibilities to and tabases>106 fin- sample: combinethe intelligence gerprints) modalities, Improve perfor- Position, Noise, Location, consideringtheir mancefor very lighting, channel, clothing availability and aging aging partialfingermarks dependence (5 -8minutiae) Forensic Build,validateand calibrate semi-automatic Explorethe Early evaluation LR-based methods possibility to developments: Improve andharmonize expert-based developsemi- trytocombine protocols automatic information Develophybrid LR-based methods (expert- LR-based fromseveral soft basedand semi-automatic) methods modalities

Table1:Challengesofdifferent biometricmodalities regardingthe forensic applications

5.6Implementation

At localand nationallevel,numerous biometricsolutions areimplemented within law enforcementbut theforensicbiometricsfield remainsafragmentedreality.For instance, countless face recognition products have been acquiredlocally,testedand implemented independently along thelastdecadetosupportforensicinvestigationand intelligence, despiteknownpoor results[Br02],confirmedagain forthe UK riotsof2011 [Fi11]. Research anddevelopmentprovidessolutions to improvethe captureofbiometric data, like intelligentcameras that can automaticallydetect, zoom in andfollowfaces.But organisationalaspects likecomplying with minimalqualityrequirementsand technical standardsare necessary to stimulatethe implementationofnew technology. AFIS systemsand computerized DNA databasesare at best operableatnationallevel. Butensuringtheir interoperability at alargerscale,likethe connectionofthe EU- nationalfingerprint andDNA databasesunderthe umbrella of thePrümTreatyorthe internationalexchange of biometricinformation through Interpol remainsachallenge. Both technologicaland organisationaldimensions provetobedifficult on alocal scale andbarelymanageableonnationaland European scales.The cause hastobefound in thenumberofparties increasinggreatly andinthe differences of culture, legislationand IT infrastructure” [PG11].

15 6Development of newapplications

Thecurrent forensic research mainly focusesonidentification, individualizationand associationatsource level, trying to answer to thequestion: whoisthe origin of atrace? Less attentionhas been giventoreconstruction at activity level, trying to answer to the questions:how andwhenthe trace wasmade? Butanalysing andinterpretatingthe positionoffingermarks,the quantity of DNA, themovement of abody or theexpression of aface to an activity is of great forensic interest.Exploitingpropertiesofbiometric traces to date is asimilar challenge, andspectroscopicpropertiesofphysical traces outside of thevisible range maycontributetoit.

Theflaws of theidentitymanagementinfrastructures andprocessesofferanewroleto play forforensicbiometrics: contributetoinvestigateidentity fraud andfindremedies againstit. Theroleisnot limited to criminal caseslikethe substitutionofconvicted persons in prison, but also extends to civilcases like family relatednessclaims (paternity,lineage)oradministrativecases like residenceorsocialbenefitsclaims. DNA can certainlyplayacentralroleinthe enrolmentphasesofthese processes, but forthe verificationphasesother modalitiesseemmoresuitablebecause of thecomplex andlong analytical processofDNA andits risk of contamination.

7. Conclusion

Making forensic biometrics one community improving thecurrent forensic biometric applications anddeveloping newforensicbiometric applications is achallenge.It necessitatescollaborationtoset up research directions embedding severalaspects, generally in thehands of different actors:the relevant data,the relevant inference models,the relevant technology andthe relevant evaluation framework.But this is the challengeidentifiedbythe European Council to face thefield of high tech andcyber crimeinits conclusion on thevisionfor European Forensic Science2020. It recognizes thecentral role of theexchange of informationincluding biometrics andother data generatedbyforensicprocessesinthe prevention of andfight againstcrime andcriminal activities. It also emphasisesthe need to define commonlyacceptedminimum forensic sciencestandardsfor thecollection, processing, useand delivery of forensic data relating interaliatodataconcerning DNA profiles,aswellasdactyloscopicand otherbiometric data.

References

[AF09] AFSP, Standards forthe formulationofevaluativeforensicscience expertopinion. Scienceand Justice, 2009(49):p.161-164. [Al08] Alberink, I., Obtainingconfidence intervals and Likelihood Ratios forbody height estimations in images.ForensicScience International, 2008(177):p.228-237. [Be12] Availablefrom: www.beat-eu.org,consulted 07.07.2012.

16 [Be86] Bertillon, A., De l'identificationpar lessignalements anthropométriques.Reprinted fromArchivesd'anthropologiecriminelleetdes sciences pénales. 1886, Paris: Masson. [Br02] Brooks,M.A., Face-off. NewScientist,2002. 175 (2399). [BS01] Berry, J. andD.A.Stoney, Historyand DevelopmentofFingerprinting,inAdvances in Fingerprint Technology,H.C.Lee andR.E.Gaensslen, Editors.2001, CRC Press: Boca Raton. p. 1-40. [BTM12] Biedermann, A.,F.Taroni,and P. Margot, ReplytoBudowle,Ge, Chakraborty and Gill-King: useofprior odds formissing persons identification. InvestigativeGenetics, 2012. 3:p.1-2. [Bu11] Budowle,B., et al., Useofprior odds formissing persons identification. Investigative Genetics,2011. 2(1-6). [CM00] Champod, C. andD.Meuwly, TheInference of Identity in ForensicSpeaker Recognition. Speech Com.,2000. 31(2-3): p. 193-203. [Co98] Cook, R.,etal., Amodelfor case assessmentand interpretation. Science&Justice, 1998. 38(3): p. 151-156. [DC08] Dessimoz,D.and C. Champod, Linkagesbetween biometrics and forensicscience,in Handbook of Biometrics,A.Jain, F. P, andA.Ross, Editors. 2008, Springer: New York.p.425-459. [Eu11] Europol, Europol InformationManagement:Products and Services,2011, Europol: TheHague.p.23. [Ev98] Evett, I., Toward auniform frameworkfor reporting opinions in forensic science casework. Science&Justice, 1998. 38(3): p. 198 -202. [Fi11] Firth, N., Face recognition technology failstofindUKrioters. NewScientist, 2011(2826). [Ga92] Galton, F., . Macmillianand Compagny, London, 1892 [Reprinted Da Capo Press,New York,1965],1892. [Go91] Good, I.J., Weight of evidence and theBayesianlikelihood ratio,inTheUse of Statistics in ForensicScience,C.G.G.Aitkenand D.A. Stoney, Editors. 1991, Ellis Horwood: Chichester,UK. p. 85–106. [GR06] Gonzalez-Rodriguez, J.,etal., Robustestimation, interpretation and assessment of likelihood ratiosinforensicspeaker recognition. Computer Speech andLanguage, 2006. 20:p.331-355. [GR12] Gonzalez-Rodriguez, J.,etal. ALinguistically-Motivated Speaker Recognition Front- Endthrough SessionVariability CompensatedTrajectories in Phone Units.in IDASSP 2012.2012. Kyoto. [Hi10] Hicks, T.,etal., UseofDNA profiles forinvestigationusing asimulated national DNAdatabase: Part I. PartialSGM Plus1profiles. Forens.Sci.Int.: Genetics,2010. 4:p.232-238. [IHK11] Indovina,M., R.A. Hicklin,and G.I. Kiebuzinski, ELFT-EFS EvaluationofLatent Fingerprint Technologies:ExtendedFeature [SetsEvaluation#1],2011, U.S. Department of Commerce,NationalInstitute of Standardsand Technology: Washington DC. [In09] Interpol, Disaster VictimsIdentificationGuide,2009, Interpol:Lyon. p. 55. [IR98] Inman, K. andN.Rudin, Theoriginofevidence. Forens.Sci.Int., 2002. 126:p.11-16. [JDN04] Jain,A., S. Dass,and K. Nandakumar, SoftBiometric Traits forPersonal Recognition Systems,inBiometricAuthentication,D.Zhang andA.Jain, Editors. 2004, Springer Verlag:Heidelberg. p. 1-40. [Kr09] Krane, D.,etal., Time forDNAdisclosure. Science, 2009. 326(18December):p.1631- 1633. [Le06] VanLeeuwen,D., et al., NIST and NFI-TNOevaluations of automatic speaker recognition. Comp.Speechand Lang.,2006. 20:p.128–158.

17 [MD01] Meuwly,D.and A. Drygajlo. Forensic Speaker RecognitionBased on aBayesian Frameworkand Gaussian MixtureModelling(GMM).in2001, ASpeechOdyssey – TheSpeaker RecognitionWorkshop.2001. Crete. [Me06] Meuwly,D., Forensic IndividualizationfromBiometric Data. Scienceand Justice, 2006. 46(4): p. 205-213. [Me10] Meuwly,D., ID management in 2020,2010, ID.academy:The Hague. p. 21. [Mn11] Mnookin, J.L.,etal., TheNeedfor aResearch CultureinThe ForensicSciences. UCLA L. Rev.,2011. 58:p.725-801. [Ne06] Neumann, C.,etal., ComputationofLikelihood RatiosinFingerprint Identification forConfigurations of Three Minutiae. J. For. Sci.,2006. 51(6): p. 1255–1266. [NES12] Neumann, C.,I.W.Evett,and J. Skerett, Quantifyingthe weight of evidence from a forensic fingerprint comparison: anew paradig. J. R. Statist. Soc. A, 2012. 175(2): p. 1-26. [PG11] Plomp, M.G.A. andJ.H.A.M.Grijpink. CombatingIdentityFraud in thePublic Domain:InformationStrategiesfor Healthcare and Criminal Justice.inProceedings of the11thEuropean Conference on e-Government.2011. Ljubljana,Slovenia: Academic ConferencesInternational(ACI). [RC07] Ramos-Castro,D., Forensicevaluationofthe evidence usingautomatic speaker recognitionsystems,2007, UniversidadAutónomadeMadrid: Madrid,Spain.p.169. [Re05] Reiss, R.A., Manuelduportraitparlé.1905, Lausanne:Th. Sack. [RWM06] Ribaux, O.,S.J.Walsh,and P. Margot, Thecontributionofforensicscience to crime analysisand investigation: Forensicintelligence. Forensic ScienceInternational, 2006(156):p.171-181. [SK05] Saks,M.and J. Koehler, Thecomingparadigm shiftinforensicidentification science. Science, 2005(309):p.892–895. [SVN07] Singh, R.,M.Vatza,and A. Noore, Intelligentbiometric informationfusionusing supportvectormachine,inSoftcomputinginimage processing: recent advances 2007, Springer-Verlag:Heidelberg. p. 325 –349. [Ta06] Taroni,F., et al., Bayesian networks and probabilistic inferenceinforensicscience. 2006, London: Wiley. [Wh10] Whither Biometrics Committee of98 NationalResearchCouncil, Biometric Recognition: Challenges and Opportunities,ed. J.N. Pato andL.I.Millett. 2010: The NationalAcademies Press.

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