automatic latent value determination...
TRANSCRIPT
ICB2016
AutomaticLatentValueDetermination
KaiCao†,TarangChugh †,JiayuZhou †,Elham Tabassi ‡ andAnilK.Jain †
†MichiganStateUniversity‡NationalInstituteofStandardsandTechnology
June14,2016
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ChallengesinLatentMatching
PoorRidgeClarity PartialRidgeArea ComplexBackground
• AFISPerformance(Rank-1accuracy)– Plain:98.5%– Latent:67.2%(70.2%withimage+markup)
C.Watson, G.Fiumara,E.Tabassi,S.L.Cheng,P.Flanagan,W.Salamon.Fingerprint VendorTechnologyEvaluation,NISTIR,8034, 2012.* M.Indovina,V.Dvornychenko,R.Hicklin, andG.Kiebuzinski.ELFT-EFSEvaluationofLatentFingerprint Technologies:Extended FeatureSets,NISTIR,2012. 3
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IndividualizationExclusion
Inconclusive
LatentMatching:ACE-V Protocol
Fingerprint examiner
ValueDetermination
FeatureMarkup
AFIS
CandidateList
C1
C2 Ck
C3..
..
Decision
ValueforIndividualization(VID)ValueforExclusionOnly(VEO)
NoValue(NV)
Retain/Discard
EvaluationVerificationbysecondexaminer
OfValue
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ComparisonAnalysis
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LimitationsofExaminerValueDetermination
• Highly subjective• repeatability(intra-examinervariability):84.6%• reproducibility(inter-examinersimilarity): 75.2%
• Dependsuponexaminer’sskillandexperience
• Time-consuming
Ulery etal.,“Repeatability andreproducibility ofdecisionsbylatent fingerprint examiners,” PloSone,7(3):e32800,2012.Ulery etal.,“Accuracyandreliability offorensiclatentfingerprint decisions,” PNAS,108(19):7733–7738, 2011.
Needforautomaticvaluedetermination
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Minutiaefeatures
Ridgefeatures
Singularityfeatures
Orientationfeatures
AutomaticFeatureExtraction
3 4 5 6 7 8 9 14 15 16 17 18 19
FeatureVector
PredictedValue TrainedSVM
LatentwithmarkedROI
𝑽𝑰𝑫
𝑽𝑰𝑫
ProposedAutomaticMethodforValueDetermination
𝑽𝑰𝑫
𝑽𝑰𝑫
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FeaturesforValueAssessmentFeatureNo. Description
1 Numberofminutiae
2- 8 Sumofminutiae reliabilitywithreliability≥t,t=0,0.1,...,0.6
9 AverageareaofminutiaeDelaunaytriangulation
10 Areaoftheconvexhullofminutiaeset
11- 17 Sumofridgequalityblockswith qualityvalue ≥t,t=0,0.1,...,0.6
18 Numberofsingularpoints(coreanddelta)
19 Standarddeviationoftheridgeflow intheforeground
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Feature Extraction
InputLatent withROI RidgeFlowEstimation Normalization
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K.CaoandA.K.Jain,LatentOrientationFieldEstimationviaConvolutionalNeuralNetwork, ICB,2015
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Image Normalization
LocalridgeorientationOriented
window
X
X-signature
maxX
minX
OrientedwindowandX-signature
Inputlatentimage
Normalizedimage 9
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Feature Extraction
RidgeEnhancement
InputLatent withROI RidgeFlowEstimation Normalization
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Ridge Enhancement
• Dictionaryconstruction• Ridgeenhancementusingdictionary
Inputlatent EnhancedbydictionaryEnhancedbyGaborfiltering
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Ridge Enhancement• Dictionaryconstruction
+=
Dictionaryelement
Valleyimage
Ridgeimage
Asubsetofdictionaryelementsusedforlatentridgeenhancement
Notethatridgeandvalleywidthscanbedifferent
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RidgeQualityEstimation
Feature Extraction
RidgeEnhancement
InputLatent withROI RidgeFlowEstimation Normalization
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Ridge Quality Estimation
(a) Normalizationofinput latent
(b)NormalizationofEnhancedlatent
(c)Orientationcoherenceof(a) (d)Orientationcoherenceof(b) (f)FusedQualitymapof(c),(d)&(e)
(e)Similaritybetween(a)and(b)
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RidgeQualityEstimationMinutiaeExtraction
Feature Extraction
RidgeEnhancement
InputLatent withROI RidgeFlowEstimation Normalization
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MinutiaeandSingularPoints Extraction
LatentID:G007 LatentID:B106 LatentID:U228
ManuallymarkedminutiaeAutomaticextractedminutiaeAutomaticdetectedcorepoint
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N.K.Ratha,S.ChenandA.K.Jain,“Adaptivefloworientation-basedfeatureextractioninfingerprintimages”,PatternRecognition, 1995
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Experiments• Latent Databases (Total of 707 latents)• NIST SD27: 258 latents• WVU Latent DB: 449 latents
• Latent value determination posed as two class(VID and not-VID) classification
• Two sources of ground truth• Value Determination by Examiners• Value Determination by latent AFIS*
• Latentmates retrieved at Rank-1 determined to be VID
• 10-fold cross validation protocol
*OneofthebestperforminglatentAFIS inELFTevaluation 17
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GroundTruthConsistency
Consistency:77.8%
Agreement
AFIS:VIDExaminer:VID
AFIS:VIDExaminer:VID
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GroundTruthConsistency
Consistency:77.8%
Disagreement
AFIS:VIDExaminer:VID
AFIS:VIDExaminer:VID
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CorrectlyClassified
Examiner:VIDOurMethod:VID
Examiner:VIDOurMethod:VID
LatentID:G020 LatentID:U204
LatentValueGroundTruthbyExaminers
ClassificationAccuracy:85.6± 2.4% 20
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IncorrectlyClassified
Examiner:VIDOurMethod:VID
Examiner:VIDOurMethod:VID
LatentID:B120 LatentID:G057
LatentValueGroundTruthbyExaminers
ClassificationAccuracy:85.6± 2.4% 21
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LatentValuegroundtruthbyAFISCorrectlyClassified
AFIS:VIDOurMethod:VID
AFIS:VIDOurMethod:VID
LatentID:G004 LatentID:U213
ClassificationAccuracy:79.5± 7.2% 22
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IncorrectlyClassified
AFIS:VIDOurMethod:VID
AFIS:VIDOurMethod:VID
LatentID:U260 LatentID:B148
LatentValuegroundtruthbyAFIS
ClassificationAccuracy:79.5± 7.2% 23
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Conclusions
• Automatic and objective value determination of aquery latent
• No manual featuremarkup is required
• Experimental results on NIST SD27 and WVU latentdatabases demonstrated the efficiency of proposedapproach
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