Human Recognition in Unconstrained Environments: Using Computer Vision, Pattern Recognition and Machine Learning Methods for Biometrics

250 Pages·2017·9.566 MB·English

by  Maria De Marsico, Michele Nappi, Hugo Pedro Proença

Human Recognition in
Unconstrained Environments
Using Computer Vision, Pattern Recognition and
Machine Learning Methods for Biometrics
Edited by
Maria De Marsico
Michele Nappi
Hugo Proença
HUMAN
RECOGNITION IN
UNCONSTRAINED
ENVIRONMENTS
This page intentionally left blank
HUMAN RECOGNITION
IN UNCONSTRAINED
ENVIRONMENTS
Using Computer Vision, Pattern
Recognition and Machine Learning
Methods for Biometrics
Editedby
MARIADEMARSICO
MICHELENAPPI
HUGOPROENÇA
AMSTERDAM • BOSTON • HEIDELBERG • LONDON • NEWYORK
OXFORD • PARIS • SANDIEGO • SANFRANCISCO • SINGAPORE
SYDNEY • TOKYO
AcademicPressisanimprintofElsevier
AcademicPressisanimprintofElsevier
125LondonWall,LondonEC2Y5AS,UnitedKingdom
525BStreet,Suite1800,SanDiego,CA92101-4495,UnitedStates
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
Copyright©2017ElsevierLtd.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,
electronicormechanical,includingphotocopying,recording,oranyinformationstorageand
retrievalsystem,withoutpermissioninwritingfromthepublisher.Detailsonhowtoseek
permission,furtherinformationaboutthePublisher’spermissionspoliciesandourarrangements
withorganizationssuchastheCopyrightClearanceCenterandtheCopyrightLicensingAgency,
canbefoundatourwebsite:www.elsevier.com/permissions.
Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe
Publisher(otherthanasmaybenotedherein).
Notices
Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience
broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical
treatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluating
andusinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuch
informationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,
includingpartiesforwhomtheyhaveaprofessionalresponsibility.
Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,
assumeanyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproducts
liability,negligenceorotherwise,orfromanyuseoroperationofanymethods,products,
instructions,orideascontainedinthematerialherein.
LibraryofCongressCataloging-in-PublicationData
AcatalogrecordforthisbookisavailablefromtheLibraryofCongress
BritishLibraryCataloguing-in-PublicationData
AcataloguerecordforthisbookisavailablefromtheBritishLibrary
ISBN:978-0-08-100705-1
ForinformationonallAcademicPresspublications
visitourwebsiteathttps://www.elsevier.com
Publisher:NikkiLevy
AcquisitionEditor:TimPitts
EditorialProjectManager:CharlotteKent
ProductionProjectManager:LisaJones
Designer:MarkRogers
TypesetbyVTeX
CONTENTS
Contributors ix
EditorBiographies xi
Foreword xiii
1. UnconstrainedDataAcquisitionFrameworksandProtocols 1
JoãoC.Neves,JuanC.Moreno,SilvioBarra,FabioNarducci,
HugoProença
1.1. Introduction 1
1.2. UnconstrainedBiometricDataAcquisitionModalities 3
1.3. TypicalChallenges 4
1.4. UnconstrainedBiometricDataAcquisitionSystems 8
1.5. Conclusions 25
References 26
2. FaceRecognitionUsinganOutdoorCameraNetwork 31
Ching-HuiChen,RamaChellappa
2.1. Introduction 31
2.2. TaxonomyofCameraNetworks 34
2.3. FaceAssociationinCameraNetworks 36
2.4. FaceRecognitioninOutdoorEnvironment 38
2.5. OutdoorCameraSystems 42
2.6. RemainingChallengesandEmergingTechniques 49
2.7. Conclusions 50
References 50
3. RealTime3DFace-EarRecognitiononMobileDevices:New
Scenariosfor3DBiometrics“in-the-Wild” 55
MicheleNappi,StefanoRicciardi,MassimoTistarelli
3.1. Introduction 55
3.2. 3DCaptureofFaceandEar:CURRENTMethodsandSuitableOptions 58
3.3. MobileDevicesforUbiquitousFace–EarRecognition 62
3.4. TheNextStep:MobileDevicesfor3DSensingAimingat3DBiometric
Applications 67
3.5. ConclusionsandFutureScenarios 70
References 72
v
vi Contents
4. AMultiscaleSequentialFusionApproachforHandlingPupil
DilationinIrisRecognition 77
RaghunandanPasula,SimonaCrihalmeanu,ArunRoss
4.1. Introduction 77
4.2. PreviousWork 82
4.3. WVUPupilLightReflex(PLR)Dataset 84
4.4. ImpactofPupilDilation 87
4.5. ProposedMethod 88
4.6. ExperimentalResults 97
4.7. ConclusionsandFutureWork 100
References 101
5. IrisRecognitiononMobileDevicesUsingNear-InfraredImages 103
HaiqingLi,QiZhang,ZhenanSun
5.1. Introduction 103
5.2. Preprocessing 105
5.3. FeatureAnalysis 109
5.4. MultimodalBiometrics 113
5.5. Conclusions 115
References 116
6. FingerphotoAuthenticationUsingSmartphoneCameraCaptured
UnderVaryingEnvironmentalConditions 119
AakarshMalhotra,AnushSankaran,ApoorvaMittal,MayankVatsa,
RichaSingh
6.1. Introduction 119
6.2. LiteratureSurvey 121
6.3. IIITDSmartPhoneFingerphotoDatabasev1 124
6.4. ProposedFingerphotoMatchingAlgorithm 126
6.5. ExperimentalResults 132
6.6. Conclusion 142
6.7. FutureWork 143
Acknowledgements 143
References 143
7. SoftBiometricAttributesintheWild:CaseStudyonGender
Classification 145
ModestoCastrillón-Santana,JavierLorenzo-Navarro
7.1. Introduction 145
7.2. BiometricsintheWild 150
7.3. GenderClassificationintheWild 152
Contents vii
7.4. Conclusions 168
References 169
8. GaitRecognition:TheWearableSolution 177
MariaDeMarsico,AlessioMecca
8.1. MachineVisionApproach 179
8.2. FloorSensorApproach 181
8.3. WearableSensorApproach 182
8.4. DatasetsAvailableforExperiments 189
8.5. AnExampleofaCompleteSystemforGaitRecognition 189
8.6. Conclusions 194
References 194
9. BiometricAuthenticationtoAccessControlledAreasThroughEye
Tracking 197
VirginioCantoni,NahumiNugrahaningsih,MarcoPorta,HaochenWang
9.1. Introduction 197
9.2. ATM-LikeSolutions 199
9.3. MethodsBasedonFixationandScanpathAnalysis 201
9.4. MethodsBasedonEye/GazeVelocity 206
9.5. MethodsBasedonPupilSize 210
9.6. MethodsBasedonOculomotorFeatures 211
9.7. MethodsBasedonHeadOrientation 213
9.8. Conclusions 213
References 214
10.NoncooperativeBiometrics:Cross-JurisdictionalConcerns 217
MarioSavastano
10.1.Introduction 217
10.2.BiometricsforImplementingBiometricSurveillance 218
10.3.ReactiontoPublicOpinion 219
10.4.TheEarlyDays 220
10.5.AnInterestingClue(2007) 224
10.6.BiometricSurveillanceToday 225
10.7.Conclusions 226
References 227
Index 229
This page intentionally left blank
CONTRIBUTORS
SilvioBarra
DepartmentofMathematicandComputerScience,UniversityofCagliari,
Cagliari,Italy
VirginioCantoni
DipartimentodiIngegneriaIndustrialeedell’Informazione,UniversitàdiPavia,
Pavia,Italy
ModestoCastrillón-Santana
SIANI,UniversidaddeLasPalmasdeGranCanaria,LasPalmasdeGranCanaria,
Spain
RamaChellappa
DepartmentofElectricalandComputerEngineering,UniversityofMaryland,
CollegePark,MD,UnitedStates
Ching-HuiChen
DepartmentofElectricalandComputerEngineering,UniversityofMaryland,
CollegePark,MD,UnitedStates
SimonaCrihalmeanu
ComputerScience&Engineering,MichiganStateUniversity,EastLansing,MI,
USA
MariaDeMarsico
DepartmentofComputerScience,SapienzaUniversityofRome,Rome,Italy
HaiqingLi
CenterforResearchonIntelligentPerceptionandComputing,Instituteof
Automation,ChineseAcademyofSciences,Beijing,PRChina
JavierLorenzo-Navarro
SIANI,UniversidaddeLasPalmasdeGranCanaria,LasPalmasdeGranCanaria,
Spain
AakarshMalhotra
IIITDelhi,Delhi,India
AlessioMecca
DepartmentofComputerScience,SapienzaUniversityofRome,Rome,Italy
ApoorvaMittal
IIITDelhi,Delhi,India
JuanC.Moreno
IT–InstitutodeTelecomunicações,UniversityofBeiraInterior,Covilhã,Portugal
MicheleNappi
DepartmentofInformationTechnology,UniversityofSalerno,Fisciano,Italy
ix