Transcript Lecture about biometrics in one hour.

Biometrics
Viktor MINKIN
[email protected]
Outline
Outline
Introduction
Biometric systems
Biometric characteristics
Fingerprints
Unimodal systems
Multi-modal systems
Problems
Links
History and future
Introduction
Biometrics [harmonized]
Automated recognition of persons based on
their biological or/and behavioral characteristics.
Automated measurement of biological or/and
behavioral characteristics of person for medical,
security or psychological purposes.
Introduction
Terms and definitions
Template
Capture
Comparison
Database
Enrollment
Matching
Token
User
Introduction
Identification of a person
– Verification/Verify
• Comparing one to one
• “Am I who I claim I am”
– Identification
• Comparing one to many
• “Who am I”
Introduction
Application
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Passport control
Access to secured areas
Surveillance
ATMs
Computer logins
E-commerce
Medicine
Psychology
Introduction
Traditional means of automatic identification
(before biometrics)
– Knowledge-based
• Use “something that you know”
• Examples: password, PIN
– Token-based
• Use “something that you have”
• Examples: credit card, smart card, keys
Introduction
Problems with traditional approaches
– Token may be lost, stolen or forgotten
– PIN may be forgotten or guessed by the imposters
• (25% of people seem to write their PIN on their ATM
card)
Estimates of annual identity fraud damages
per year:
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$1 billion in welfare disbursements
$1 billion in credit card transactions
$1 billion in fraudulent cellular phone use
$3 billion in ATM withdrawals
Introduction
The traditional approaches are unable to differentiate
between an authorized person and an imposter
Use biometrics which relies on “who you are” or
“what you do”
Biometric Systems
Requirements for an ideal biometric
– Universality
• Each person should have the characteristic
– Uniqueness
• No two persons should be the same in terms of the
characteristic
– Permanence
• The characteristic should not change
Biometric Systems
Issues in a real biometric system
– Performance
• Identification accuracy, speed, robustness, resource
requirements
– Acceptability
• Extend to which people are willing to accept a particular
biometric identifier
– Faked protection
• How easy is it to fool the system by fraudulent methods
Biometric Systems
Identification accuracy
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FAR = false acceptance rate
FRR = false rejection rate
EER = equal error rate
TER = total error rate = FAR + FRR
FER= false enrollment rate
Biometric Systems
False Acceptance Rate
Receiver operating characteristics (ROC)
Equal Error Rate
False Rejection Rate
Biometric Systems
FAR/FRR and comparison threshold
Biometric Characteristics
Static (biological) parameters
Fingerprints
Face
Iris
Hand geometry / vein
Retinal pattern
Facial thermogram
Lip information
DNA
Biometric Characteristics
Dynamic (behavior) biometric parameters
Signature
Voice
Motion
Pulse
Biometric Characteristics
Market Shares
Biometric Characteristics
Market development
Fingerprints
Accurate
Comparatively cheap hardware
Questionable acceptance
Fingerprints
Optical technology
Light
source
Finger
Prism
Lens
Video Camera (CCD)
Light reflects from the surface of the prism where the finger is not
in contact with it, while it penetrates the surface of the prism
where the finger touches the surface of the prism. The resulting
image goes through a lens into a video camera.
Fingerprints
Capacity technology
Fingerprints
Fiber optic technology
Fingerprints
Fingerprint types
Arches
Loops
Whorl
Minutia types
Bridge
Dot
Ridge
Ending
Bifurcation
Enclosure
Fingerprints
Core & Deltas
Fingerprints
Fingerprint minutiae
Fingerprints
Image transformation
Source
FFT
Flow field
Directional
image 1
Directional
image 2
Directional
irregularity
Code
Smoothing
Binarization
Skeleton
formation
Skeleton
cleaning
Minutiae
search
Fingerprints
Comparative testing
Fingerprints
Fingerprint information
Unimodal Systems
Facial ID
Illumination
Head pose
Occlusion
Unimodal Systems
Hand Vein
Hand geometry
Questionable accuracy
Unimodal Systems
Retinal Pattern
Highest accuracy
Even more intrusive than iris recognition
Unimodal Systems
Facial Thermo image and VibraImage
Non-intrusive
View-dependent
Depends heavily on
human factors,
body temperature
Lie detection
Emotion control
Criminals detector
Medical monitoring
Psychology testing
Multi-modal Systems
Why multimodal [multiple] person
identification?
– Quest for non-intrusive identification methods
• No special purpose hardware needed
• Works potentially at greater distances
– “Traditional” arguments for going multimodal:
• Increasing performance
• Increasing robustness
– Mono-modal recognition techniques are likely to
reach in a close future a saturation in
performance.
Multi-modal Systems: Fusion
“Early integration” or “sensor fusion”
Integration is performed on the feature level
Classification is done on the combined feature vector
Features
Modality 1
Features
Modality 2
Features
Modality n-1
Features
Modality n
Classifier
Identity
Multi-modal Systems
3
-Elsys includes
BiCard, VibraImage, BioFinger
3D-Elsys is biological and behavioral identification system
Multi-modal Systems
The World population in 2000 was about
6.000 M. people.
The biometric document (ID card) market is
more than $6.000.000.000
There are 3 different ID card technologies:
1. Card with additional memory (chip, CD,..)
2. Card with 2d-bar code
3. BiCard (3D-Elsys)
Problems
Errors rate
Misunderstanding of real advantages and
problems
Incomplete true about biometric systems
Links
International Biometric Group
- http://www.biometricgroup.com
NIST
- http://www.itl.nist.gov/div893/biometrics/
Literature
– http://www.itl.nist.gov/iaui/894.03/pubs.html#fing
Patents
- http://www.elsys.ru/patents.php
Biometrics evolution
19 century- not automated identification
20 century- biometric identification
21 century- emotion recognition and detection
Viktor Minkin
Biometrics
[email protected]
Thank you!
2004