1. An Introduction to Biometric Identity Verification
Gérard CHOLLET
GET-ENST/CNRS-LTCI 46 rue Barrault PARIS cedex 13

2. 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

3. 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

4. Why is it important to recognize the 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)

5. 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

6. 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)

7. Modalities for identity verification
Bla-bla
SECURED
SPACE
PIN

8. Physical Biometric Modalities
Face (visible light, infra-red, thermogram, 3D, …)
Finger print
Retinal scan, Iris
Hand geometry, Veins, Palm print
Ear shape,
Genetic code
...

9. Behavioral Biometric Modalities
Speech (text dependent, text independent, …)
Hand writing, signature
Gesture, Gait
Keystroke pattern on a keyboard …

10. 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)

11. Empreintes digitales

12. Empreintes digitales

13. Minuties

14. Le visage
                           

15. Caméra infra-rouge

16. Erosion and sharpening Simplified image
Best-fit
ellipse image
Rotation
Normalized image
Erosion and sharpening
Simplified image
Gradient image
Adaptive Hough transform and template matching
Snake energy:

17. Normalisation du contraste
Initial Images
After Normalization

18. Rétine

19. Localisation de l’iris

20. Iris

21. Comparaison des caractéristiques de l’iris

22. Signatures

23. La démarche

24. Speaker Verification Typology of approaches (EAGLES Handbook)
Text dependent
Public password
Private password
Customized password
Text prompted
Text independent
Incremental enrolment
Evaluation

25. Architecture d’un système de reconnaissance biométrique

26. 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.

27. 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

28. Decision theory for 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

29. Decision for Identity Verification
Likelihood ratio

30. Distribution des scores

31. Receiver Operating Characteristic (ROC) curve

32. Detection Error Tradeoff (DET) Curve

33. Speaker Verification Typology of approaches (EAGLES Handbook)
Text dependent
Public password
Private password
Customized password
Text prompted
Text independent
Incremental enrolment
Evaluation

34. Inter-speaker VariabilityWe
were
away a
year
ago.

35. Intra-speaker VariabilityWe
were
away a
year
ago.

36. Dynamic Time Warping (DTW)

37. HMM structure depends on the application

38. Signal detection theory

39. Speaker Verification (text independent)
The ELISA consortium
ENST, LIA, IRISA, ...
NIST evaluations

40. Gaussian Mixture Model
Parametric representation of the probability distribution of observations:

41. Gaussian Mixture Models
8 Gaussians per mixture

42. National Institute of Standards & Technology (NIST) Speaker Verification Evaluations
Annual evaluation since 1995
Common paradigm for comparing technologies

43. GMM speaker modeling WORLD GMM MODEL TARGET GMM MODEL GMM MODELING
WORLD DATA
TARGET
SPEAKER
Front-end
GMM MODELING
WORLD GMM MODEL
GMM model adaptation
TARGET GMM MODEL

44. Baseline GMM method l WORLD GMM MODEL HYPOTH. TARGET GMM MOD. =
Front-end
WORLD GMM MODEL
Test Speech =
LLR SCORE

45. 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

46. SVM principles X y(X) Feature space Input space H Class(X) Ho
Separating hyperplans H , with the optimal hyperplan Ho Ho H
Class(X)

47. Results

48. 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

49. 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.

50. Presentations to come

51. Perspectives
A lot of interest from governments, telecom and financial operators,…
Fusion of modalities.
A number of R&D projects within the EU.
Smart cards to support biometric references and to perform identity verification.