An Introduction to Biometric Identity Verification Gérard CHOLLET [email protected]@...
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Transcript of An Introduction to Biometric Identity Verification Gérard CHOLLET [email protected]@...
An Introduction to Biometric Identity Verification
Gérard [email protected]
GET-ENST/CNRS-LTCI46 rue Barrault
75634 PARIS cedex 13http://www.tsi.enst.fr/~chollet
Outline
What is Biometry ? Why is it important ? Biometric modalities, Physical and behavioral
characteristics Pattern recognition and Decision theory Multimodal Identity Verification Databases, Evaluation, Standardization Applications Introduction to further presentations Perspectives
What is BIOMETRICS ?
This term has several meanings : statistical and mathematical methods applicable
to data analysis problems in the biological sciences
Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic.
The second meaning is of concern here. It is a hot topic for security and prevention of
identity theft
Why is it important to recognizethe identity of a person ?
Identification and/or Verification Protection of individual property (habitation,
bank account, personal data, messages, mobile phone, PDA,...)
Limited access (secured areas, data bases) Locate a particular person in an audio-visual
document (information retrieval) Who is speaking in a meeting ? Is a suspect the criminal ? (forensic
applications)
How to verify the identity of a person ?
Control a specific knowledge (password, PIN,...) or the possession of a document (passport, ID card) or a physical
qui risque d’être oublié par son propriétaire ou usurpé par un imposteur
contrôler une possession (passeport, clé, badge,...) qui risque d’être volé
mesurer les caractéristiques physiques (visage, empreintes digitales, iris,...) ou comportementales (parole, signature,...) de l’individu
une combinaison de ces moyens rend l’imposture difficile mais complique l’accès
Modalities for Identity Verification
A device (key, smart card,…) or a document (passport, ID card) you own
A code you remember (password, …) Could be lost or stolen
Physiological characteristics: Face, iris, finger print, hand shape,…
Need special equipments Behavioral characteristics:
Speech, signature, keystroke, gait,… Speech is the prefered modality over the telephone(but a ‘voice print’ is much more variable than a finger
print)
Physical Biometric Modalities
Face (visible light, infra-red, thermogram, 3D, …)
Finger print Retinal scan, Iris Hand geometry, Veins, Palm print Ear shape, Genetic code ...
Behavioral Biometric Modalities
Speech (text dependent, text independent, …) Hand writing, signature Gesture, Gait Keystroke pattern on a keyboard …
Desired properties of a Biometric modality
Easy to measure (for real time verification) Efficient (precision, speed, cost) unicity (2 persons should not have identical
characteristics) sustainable (NO temporal drift) User acceptance impossible to duplicate (robustness to forgery)
Best-fit ellipse image
Rotation
Normalized imageErosion and sharpening
Simplified image
Gradient image
Adaptive Hough transform and
template matchingSnake energy:
exttotal EEE int
Speaker Verification
Typology of approaches (EAGLES Handbook) Text dependent
Public password Private password Customized password Text prompted
Text independent Incremental enrolment Evaluation
Registration of a new client
Acquisition of biometric patterns to be used as reference.
For a number of modalities (signature, vocal password,...), several repetitions are desired.
A reference model may be infered from the reference patterns.
This model could be adapted to follow temporal drifts.
Recognition of a person
Is he really the person he claims to be ? Identity verification
Who am I ? Identification (the closest person in a closed set) Followed by verification to reject unknown
individuals Deliberate imposture is a major problem in
identity verification
Two types of errors : False rejection (a client is rejected) False acceptation (an impostor is accepted)
Decision theory : given an observation O and a claimed identity H0 hypothesis : it comes from an impostor H1 hypothesis : it comes from our client
H1 is chosen if and only if P(H1|O) > P(H0|O) which could be rewritten (using Bayes law) as
Decision theory for identity verification
)1()(
)(
)1(
HPHoP
HoOP
HOP
)1()(
)(
)1(
HPHoP
HoOP
HOP
Speaker Verification
Typology of approaches (EAGLES Handbook) Text dependent
Public password Private password Customized password Text prompted
Text independent Incremental enrolment Evaluation
Speaker Verification (text independent)
The ELISA consortium ENST, LIA, IRISA, ... http://www.lia.univ-avignon.fr/equipes/RAL/elisa/
index_en.html
NIST evaluations http://www.nist.gov/speech/tests/spk/
index.htm
National Institute of Standards & Technology (NIST)
Speaker Verification Evaluations
• Annual evaluation since 1995• Common paradigm for comparing technologies
GMM speaker modeling
Front-endGMM
MODELING
WORLDGMM
MODEL
Front-end GMM model adaptation
TARGETGMM
MODEL
Baseline GMM method
HYPOTH.TARGET
GMM MOD.
Front-end
WORLDGMM
MODEL
Test Speech
xPxPLog ]
)/()/([
LLR SCORE
)/( xP
)/( xP
=
Support Vector Machines and Speaker Verification
Hybrid GMM-SVM system is proposed
SVM scoring model trained on development data to classify true-target speakers access and impostors access,using new feature representation based on GMMs
Modeling
Scoring
GMM
SVM
SVM principles
X (X)
Inpu
t sp
ace
Feat
ure
spac
e Separating hyperplans H , with the optimal hyperplan Ho
Ho
H
Class(X)
Multimodal Identity Verification
M2VTS (face and speech) front view and profile pseudo-3D with coherent light
BIOMET:(face, speech, fingerprint, signature, hand shape) data collection reuse of the M2VTS and DAVID data bases experiments on the fusion of modalities
BIOMET
An extension of the M2VTS and DAVID projects to include such modalities as signature, finger print, hand shape.
Initial support (two years) is provided by GET (Groupement des Ecoles de Télécommunications)
Emphasis will be on fusion of scores obtained from two or more modalities.