Table Of Content

Age Factors in A Biometric Processing g e F a c t o r s i n As biometrics-based identification and identity authentication become Michael Fairhurst is Professor increasingly widespread in their deployment, it becomes correspondingly of Computer Vision in the B important to consider more carefully issues relating to reliability, usability School of Engineering and i and inclusion. One factor which is particularly important in this context is Digital Arts at the University of o that of the relationship between the nature of the measurements extracted Kent, UK. He leads a research m group with broad interests in the from a particular biometric modality and the age of the sample donor, and fundamental processes of image e the effect which age has on physiological and behavioural characteristics analysis and pattern recognition, t invoked in a biometric transaction. and with a particular focus on ri In Age Factors in Biometric Processing an international panel of experts applications in security and, c explore the implications of ageing on biometric technologies, and how such especially, biometrics. He is a P factors can be managed in practical situations. Topics include understanding committee member of numerous the impact of ageing on biometric measurements; age factors as barriers/ conferences, workshops, ro opportunities in relation to performance; modality-related approaches to networking and educational c activities internationally, and has management of age factors; implications for practical application; and future e published widely in the research trends and research challenges. s literature. He is a Fellow of the s Age Factors in Biometric Processing provides an outstanding overview of International Association for i this topic for the rapidly expanding community of stakeholders in biometrics- Pattern Recognition and is Editor- n based identification solutions in academia, industry and government. in-Chief of IET Biometrics. g Age Factors in Biometric Processing E d it e d b y Edited by F a The Institution of Engineering and Technology irh Michael Fairhurst www.theiet.org u r 978-1-84919-502-7 st Age Factors.indd 1 12/07/2013 15:50:41 Age Factors in Biometric Processing Age Factors in Biometric Processing Edited by Michael Fairhurst The Institution ofEngineeringand Technology PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom TheInstitutionofEngineeringandTechnologyisregisteredasaCharityin England&Wales(no.211014)andScotland(no.SC038698). †TheInstitutionofEngineeringandTechnology2014 Firstpublished2013 ThispublicationiscopyrightundertheBerneConventionandtheUniversalCopyright Convention.Allrightsreserved.Apartfromanyfairdealingforthepurposesofresearch orprivatestudy,orcriticismorreview,aspermittedundertheCopyright,Designsand PatentsAct1988,thispublicationmaybereproduced,storedortransmitted,inany formorbyanymeans,onlywiththepriorpermissioninwritingofthepublishers,orin thecaseofreprographicreproductioninaccordancewiththetermsoflicencesissued bytheCopyrightLicensingAgency.Enquiriesconcerningreproductionoutsidethose termsshouldbesenttothepublisherattheundermentionedaddress: TheInstitutionofEngineeringandTechnology MichaelFaradayHouse SixHillsWay,Stevenage Herts,SG12AY,UnitedKingdom www.theiet.org Whiletheauthorsandpublisherbelievethattheinformationandguidancegiveninthis workarecorrect,allpartiesmustrelyupontheirownskillandjudgementwhenmaking useofthem.Neithertheauthorsnorpublisherassumeanyliabilitytoanyoneforany lossordamagecausedbyanyerrororomissioninthework,whethersuchanerroror omissionistheresultofnegligenceoranyothercause.Anyandallsuchliabilityis disclaimed. Themoralrightsoftheauthorstobeidentifiedasauthorsofthisworkhavebeen assertedbytheminaccordancewiththeCopyright,DesignsandPatentsAct1988. BritishLibraryCataloguinginPublicationData AcataloguerecordforthisproductisavailablefromtheBritishLibrary ISBN978-1-84919-502-7(hardback) ISBN978-1-84919-503-4(PDF) TypesetinIndiabyMPSLimited PrintedintheUKbyCPIGroup(UK)Ltd,Croydon Contents Section 1 Introductionandbasicissues 1 1 Ageing andbiometrics: anintroduction 3 Michael Fairhurst 1.1 Introduction 3 1.2 Acontext for understandingageing effects 5 1.3 Biometrics and ageing 6 1.4 Some practical implications for biometrics 7 1.5 Plan and structure of the book 13 References 15 2 Review of ageing withrespect to biometrics anddiverse modalities 17 AndreasLanitis, Nicolas Tsapatsoulis andAnastasiosMaronidis 2.1 Introduction 17 2.1.1 Overview of biometric systems 17 2.1.2 Ageing variation 18 2.1.3 Biometric systems and ageing variation 18 2.2 Ageing databases 19 2.2.1 Face datasets 20 2.2.2 Other datasets 20 2.3 Biometric modalities and ageing 22 2.3.1 Face 22 2.3.2 Fingerprints 24 2.3.3 Hand biometrics 25 2.3.4 Voice 26 2.3.5 Behaviour 27 2.3.6 Iris 28 2.3.7 Other modalities 29 2.4 Conclusions, summary and future research directions 30 References 31 3 Biometrics andageing: social andethical considerations 37 Andrew P. Rebera andEmilio Mordini 3.1 Introduction 37 3.2 The nature of the problem 38 vi Age factors in biometric processing 3.2.1 The vulnerabilities of tokens 39 3.2.2 Ageing and age as ‘problems’ forbiometrics 41 3.3 Social and ethical factors 43 3.3.1 Social and ethical factors: problems of age 44 3.3.2 Social and ethical factors: the problem of ageing 50 3.4 Asketch of an approach to‘age-blind’ biometrics 55 Acknowledgements 58 References 58 4 Usingage to enhance performance inbiometrics 63 Ma´rjory DaCosta-AbreuandMichael Fairhurst 4.1 Introduction 63 4.2 Fusionof relevant information 64 4.3 Abrief survey of the literature 65 4.4 Representationof age information 67 4.5 System designusingage information 68 4.5.1 Age as an extra inputfeature 69 4.5.2 Age as a tool for feature selection 71 4.5.3 Additionof age-prediction classifiers 72 4.5.4 Majority weighted vote-basedfusion method 74 4.5.5 Weighted sum-based fusion method 76 4.5.6 Multiagent approach using age information 78 4.6 Experimental results and discussion 81 4.7 Conclusions 85 References 86 Section 2 Modality-related approachesto management of age factors 91 5 Humanface ageing: aperspective analysis from anthropometry andbiometrics 93 KarlRicanek Jr.,Gayathri Mahalingam, A. Midori Albert andRichard W.Vorder Bruegge 5.1 Introducing the face 93 5.2 Physical manifestations of facial ageing 94 5.2.1 Growth and development 94 5.2.2 Age-related craniofacial morphological changes: young adult to senescence 97 5.3 Computerized modeling of physical manifestations of facial ageing: a survey 101 5.3.1 Introduction 101 5.3.2 Ageing datasets 103 5.3.3 Survey 106 5.4 Conclusion 112 References 113 Contents vii 6 Ageing inbiometrics: acase studyinonline signature 117 Javier Galbally, MarcosMartinez-DiazandJulianFierrez 6.1 Introduction 117 6.2 Related works 119 6.3 The on-line signature long term database 120 6.4 Experimental protocol 123 6.5 Results 125 6.5.1 Ageing experiments 125 6.5.2 Template update experiments 126 6.6 Conclusions 128 Acknowledgements 129 References 129 7 Ageing iniris biometrics 133 JudithLiu-Jimenez andRaul Sanchez-Reillo 7.1 Anatomy of the eye 134 7.2 Irisbiometric scheme 136 7.3 Irisageing 137 7.4 Sensor 138 7.5 Quality measures 139 7.6 Pre-processing 140 7.7 Feature extractor and matching 141 7.8 Discussionand future work 149 7.8.1 Other important considerations 150 References 151 8 Ageing effects infingerprint biometrics 153 AndreasUhlandPeter Wild 8.1 Introduction 153 8.2 Related work infingerprint ageing 154 8.3 System and setup 157 8.3.1 Acquisition and preprocessing 158 8.3.2 Fingerprint extractionand comparison 158 8.4 Ageing experiments 159 8.4.1 The effect of age groupsonfingerprint performance 159 8.4.2 The effect of template-ageing on fingerprint performance 160 8.4.3 Fingerprint ageing goats 164 8.4.4 Ageing and quality 164 8.5 Conclusion 168 References 168 9 The impact of ageing onspeech-basedbiometric systems 171 FinnianKelly andNaomi Harte 9.1 Introduction 171 9.2 Speech changes with age 171 viii Age factors inbiometric processing 9.3 Age in speaker verification 172 9.4 Speaker ageing data 173 9.4.1 Ageing UBMdatabase 175 9.5 Ageing speaker verification experiment 175 9.5.1 Feature extraction and the GMM-UBMsystem 176 9.5.2 Ageing speaker verification evaluation 176 9.6 Effect of quality variation onspeaker verification 178 9.6.1 SNR 178 9.6.2 Skewness 178 9.6.3 Kurtosis 179 9.6.4 UBMlikelihood 179 9.6.5 Wnorm 179 9.6.6 Quality evaluation 180 9.7 Ageing-quality stacked classifier 180 9.7.1 Stacked classifier framework 180 9.7.2 Stacked classifier experimental evaluation 181 9.8 Conclusions 182 References 182 10 Ananalysis of biometric performance change over time: amultimodal perspective 185 NormanPoh,Josef Kittler, Chi-Ho ChanandMedhaPandit 10.1 Introduction 185 10.2 User-specific performance characterisation 186 10.3 Ourframework: a homomorphic usersgrouping algorithm 188 10.4 Experiment setup 192 10.4.1 Database and experimental protocol 192 10.4.2 Face classifiers 193 10.4.3 Speech classifier 193 10.4.4 Fusionclassifier 193 10.5 Results 194 10.5.1 Results I: Model fitting 194 10.5.2 Results II: Partitioned subjects 195 10.6 Conclusions 196 Appendix A:The face classifier 197 A.1 Local binary pattern 197 A.2 Local phase quantisation pattern 198 A.3 Multiscale pattern histogram 199 A.4 Image frame matching 199 A.5 Video face matching 200 Acknowledgement 200 References 200 Contents ix Section 3 Applicationsandimplications for practical applications 203 11 Anindustrial perspective onbiometric age factors 205 Alastair Partington 11.1 Industrial implications of biometric ageing – a risk-based view 205 11.2 Biometric ageing –doesit matter? 205 11.3 Industry: at the starting line for biometric ageing 208 11.4 Factorsexacerbating age-related biometric matching inaccuracies in industrial systems 208 11.4.1 Primary factors 209 11.4.2 Secondary factors 210 11.5 Alow-impact scenario: studentidentification 212 11.5.1 Primary factors 212 11.5.2 Secondary factors 213 11.5.3 Risk assessment 213 11.6 Amoderate-impact scenario: ePassport gates 213 11.6.1 Primary factors 213 11.6.2 Secondary factors 214 11.6.3 Risk assessment 214 11.7 Ahigh-impact scenario: anticipated useof India’s Unique IDscheme ‘Aadhaar’ 214 11.7.1 Primary factors 215 11.7.2 Secondary factors 215 11.7.3 Risk assessment 216 11.8 Possible mitigations to age-related biometric matching inaccuracies in industrial systems 216 11.8.1 Age impact minimisationby design 216 11.8.2 Age-related change compensation 218 11.9 Closing thoughts 219 References 219 12 Fingerprints andhumaninspection: aforensics perspective 221 Clive Reedman 12.1 Introduction 221 12.2 Abrief history of modern human identification methodologies 222 12.3 Physiology of fingerprints 224 12.4 Physiological effects of ageing onfingerprints 225 12.5 Ageing effects across the fingerprint matching modalities 227 12.6 Issuessurrounding the studyof fingerprint ageing 229 12.7 Conclusions 230