Biometric Technologies
Introduction to Biometrics
Fingerprint Recognition
Eye – Retinal or Iris Recognition
Dynamic Signature
Face Recognition
Summary of Biometrics
Team 3:
Steven Golikov
Barbara Edington
Melanie Johnson
Bashir Amhed
Borming Chiang
Introduction to Biometrics
History of Biometrics
Biometrics is the study of biological “data”
Biometrics has a very long tradition
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The Egyptians used the length of a person’s forearm
to determine their identification for wage payment
There are many different biometrics used for
identification:
Fingerprints
Eye – Retinal or Iris
Facial Recognition
Voice
Signature
Dental
DNA
www.biometricscatalog.org
Iris and Retinal Scanning
The Basics of Human Eye
Rationales
How do the Technologies Work?
Applications
Performance Metrics
Pros and Cons
Commercial Products
The Basics of Human Eye
Iris –
•The Shutter of our biological
camera
•The plainly visible colored ring
underneath cornea.
•Iris surrounds the pupil
Retina –
•The film of the camera
•A muscular structure which controls
the amount of light entering into the
eye, and it has very intricate details
such as colors, striations, pits and
furrows.
•Located in the back of the eye
where the Optic nerve connects.
•The blood vessels pattern in the
retina are unique to each
individual.
Source: http://www.stlukeseye.com/Anatomy.asp
Rationales for Use
Iris contains intricate details such as striations,
pits and furrows.
Two Iris’s are not alike. There is no detailed
correlation between the patterns of identical
twins or even between the left and right eye
of the same individual.
Expressed by the Individual’s Phenotype, Not
Genotype
Iris Collage
Retinal Image - Twin 1
Retinal Image -Twin 2
The patterns of blood vessels in the retina is extremely unique to individuals.
There is no detailed correlation between the patterns of identical twins or even
between the left and right eye of the same individual.
Source of images: http://www.cl.cam.ac.uk/users/jgd1000/
How does Iris Recognition Works?
A picture of the eye is taken from within 1< meter
distance and Iris portion is extracted
An Iris code of 512 Bytes is generated using
functions called 2-D wavelets. This code is unique
to one eye of one individual.
Iris code is then compared to other Iris codes that are
stored in the database
Source of images: http://www.iridiantech.com/
History of Iris Recognition
•1936 – Idea proposed by ophthalmologist Frank Burch
1949 - The idea documented in an ophthalmology
textbook by James Doggarts
1980's - The idea had appeared in James Bond films,
but it still remained science fiction and conjecture.
1987 - two ophthalmologists, Aran Safir and Leonard
Flom, patented this idea
1989 - John Daugman (then teaching at Harvard
University) try to create actual algorithms for iris
recognition
John Daugman algorithms patented in 1994, are the
basis for all current iris recognition systems and
products
Commercial Applications -Iris
The major applications of this technology so far have been:
•Aviation security and controlling access to restricted areas at
airports
London Heathrow, Amsterdam Schiphol, Frankfurt, Athens, and several
Canadian airports, Charlotte/Douglas International Airport in North
Carolina
•Database access and computer login
•Access to buildings and homes
•Hospital settings, including mother-infant pairing in maternity wards
•Border control "watch list" database searching at border crossings
On the Pakistan Afghanistan border, the United Nations High
Commission for Refugees uses these algorithms for anonymous
identification of returning Afghan refugees receiving cash grants at
voluntary repatriation centres
•Other law enforcement agency programs such Jail Security
Prisoner Identification – 1994 - Lancaster County Prison in
Pennsylvania became the first correctional facility to employ the
technology.
Performance Comparison
Method
Coded Pattern
Misidentification
rate
Security
Applications
Iris/Retinal
Recognition
Iris code / Blood
vessel pattern
1/1,200,000
High
High-security
facilities
Fingerprinting
Fingerprints
1/1,000
Medium
Universal
Hand Shape
Size, length and
thickness of
hands
1/700
Low
Low-security
facilities
Facial Recognition
Outline, shape
and
distribution of
eyes and nose
1/100
Low
Low-security
facilities
Signature
Shape of letters,
writing order,
pen pressure
1/100
Low
Low-security
facilities
Voiceprinting
Voice
characteristics
1/30
Low
Telephone
service
Source: AIM Japan, Automatic Identification Seminar, Sept.14, 2001
Pros and Cons
Iris Scanning
The uniqueness of Irises, even
between the left and right eye of
the same person, makes iris
scanning very powerful for
identification purposes.
The likelihood of a false positive is
extremely low and its relative
speed and ease of use make it a
great potential biometric.
The only drawbacks are the
potential difficulty in getting
someone to hold their head in the
right spot for the scan if they are
not doing the scan willingly.
Retina Scanning
Retina scan devices are probably
the most accurate biometric
available today. The continuity of
the retinal pattern throughout life
and the difficulty in fooling such a
device also make it a great longterm, high-security option.
The high cost of the proprietary
hardware as well as the inability to
evolve easily with new technology
make retinal scan devices a bad fit
for most situations.
It also has the stigma of
consumer's thinking it is potentially
harmful to the eye, and in
general, not easy to use.
Commercial Products and Vendors
Iris scanning (very accurate,
expensive)
Argus Solutions (Australia)
http://www.argus-solutions.com
Aurora Computer Services Ltd
(Northampton, U.K.)
Eye Ticket Corp. (Virginia, U.S.A.)
Iridian Technologies [(formerlyIriScan,
Inc.) Marlton, NJ, U.S.A. and Geneva,
Switzerland
Saflink (Redmond, WA, U.S.A.)]
Retinal scanning (very
accurate, very expensive) Retinal is more intrusive than
iris recognition.
Eyedentify, Inc. (Delaware, U.S.A.)
Microvision, Inc. (WA, U.S.A.) (RSD =
Retinal Scanning Display)
Retinal Technologies, Inc. (MA, U.S.A.)
Face Recognition
Background
Algorithms
FERET/FRVT
Research
Commercial Products
Face Recognition: The Basics
In simplistic form,
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A signature is created from a sensor’s
observation
An algorithm normalizes the signature
A matcher compares the normalized structure
to the database.
The Algorithms
Eigenfaces
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Standard Principle Components Analysis (PCA)
PCA & LDA
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LDA: Linear Discriminant Analysis
Combination based on the University of Maryland algorithm
tested in FERET.
Baysian
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An Intrapersonal/Extrapersonal Image Distance Classifier based
on the MIT algorithm tested in FERET.
Elastic Bunch Graphing
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Based on the USC algorithm tested in FERET
Uses localized landmark features represented by Gabor jets
Elastic Bunch Graphing
FERET and FRVT
FERET
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DARPA and Army Research Laboratory
1994-1996
A unified means of testing algorithms for easier
comparison
FRVT
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Designed by Govt and Law enforcement agencies
2000 and 2002
Tested ability to compare images to those stored in a
database
Females and younger people were harder to
recognize
Current Research
3-D morphable models
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Not as affected by lighting and pose as is 2-D
MERL (Mitsubishi) and Ohio State U
Identical twin Israeli students
Created a 3-D scanner that uses light to scan the image
Algos measure the distances between points and compare to
database images
Factors
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False positives
Privacy issues
Environment – lighting, movement, etc
Commercial Product
FaceIT (from Visionics / Identix)
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$100
Developed from an algorithm out of Rockefeller
University
Viisage
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From MIT algorithm based on eigenfaces
TrueFace (from Miros then acquired by Sol
Universe)
FaceOK – (from Titanium Technology)
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$89
PC user security
Introduction to Fingerprint
Recognition
Fingerprint is the most referred biometric mechanism
used today.
Fingerprint has the uniqueness feature – the studies
shows that chance of same fingerprint between two
individuals (even in twins) is one in one billion.
Fingerprint has been widely adopted (low cost) for
authentication, identification and criminal
investigation.
Uniqueness of Fingerprint
Fingerprint is unique because of the two distinct
feature
 Persistence – the basic characteristic of fingerprint
do not change in time.
 Individuality – one over 1 billions !!
Fingerprints are comprised of various types of ridge
patterns: left loop, right loop, arch, whorl and tented
arch.
The discontinuities that interrupt these smooth ridge
patterns are called Minutia. Minutiae are essentially
terminations and bifurcations of the ridge lines that
constitute a fingerprint pattern
Fingerprint
Capturing and Analysis
Fingerprint Matching
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Minutae-based
Correlation based - requires precision location
of registration point
Fingerprint Classification
The technique to assign a fingerprint into one of
the several pre-specified types already established
with indexing mechanism
Fingerprint Enhancement
It is essential to incorporate a fingerprint
enhancement algorithm with respect to the quality of
the fingerprint images in the minutiae extraction
module in order to ensure the accuracy of automatic
fingerprint identification/verification
The Identification Workflow of
Fingerprint Device
Demonstration
Identix BioTouch® 200 USB
Fingerprint Reader
Hardware BioTouch® 200 USB
Fingerprint Reader
Software - BioLogon
for Windows
Dynamic Signature
Verification
What is It?
Uses
Advantages
Disadvantages
Future
What is It?
On-line vs Off-line signature Verification
Vision-Based, Non-Vision
Dynamic Signature – unique as DNA
Measures speed, pressure of the pen
Captures x, y, z location of the writing
Uses
Point of Sale applications
Workflow automation
Security
Authentication – replaces password,
PIN, keycards, identification card
Financial – account opening, withdrawal
Wireless device security
Advantages
Signatures already accepted as a means of
identification so people willing to accept electronic
signature.
Changes in signing are consistent and have
recognizable pattern.
Is not forgotten, lost, or stolen, so simple and natural
way for enhanced computer security and document
authorization.
unique to an individual and almost impossible to
duplicate.
Disadvantages
Secured authentication
Difficult to segment strokes as writing
styles are varied and have no set
standard
Electronic tablets or digitizers are bulky
and complex.
Future
Administrative Simplification (AS) of the Health
Insurance Portability and Accountability Act (HIPAA)
IT expenditures
Frost & Sullivan - $5.7M in 2003 up to $123.3M by
2009
Mobile phones, Internet, tablet PC’s
PC/Network Access, e-Commerce and telephony,
physical access and surveillance businesses
Thank You
Have any questions or comments?
Team 3:
Steven Golikov
Barbara Edington
Melanie Johnson
Bashir Amhed
Borming Chiang