Transcript Pattern Recognition

Pattern Recognition
Biometrics
1/6/2009
Instructor:
Wen-Hung Liao, Ph.D.
Outline
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Basic Concepts
Fingerprint
Iris Scan
Hand Geometry
Face Recognition
Identification vs Verification
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Identification: Who am I? One-to-many
search
Verification: Am I who I claim I am? One-toone search
Detection: Find out whether there is an
instance of a given type of object in an
environment.
Recognition: detection + identification
Terminology
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False Acceptance Rate (FAR) : the probability
that a biometric device will allow a ‘bad guy’ to
pass. Related to security.
False Rejection Rate (FRR):the probability that a
biometric device won't recognize a good guy.
Related to convenience.
The point where false accept and false reject
curves cross is called the "Equal Error Rate."
The Equal Error Rate provides a good indicator
of the unit's performance. The smaller the Equal
Error Rate, the better.
Validity of Test Data
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Testing biometrics is difficult, because of the
extremely low error rates involved.
Some are based on theoretical models.
Some are obtained from actual field testing.
It's important to remember that error rates
are statistical: they are derived from a series
of transactions by a population of users.
What is a good biometric feature?
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Uniqueness
Invariance
Non-intrusive
Easy (or not too difficult) to acquire
Low processing cost
Fingerprint
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Finger-scan biometrics is based on the
distinctive characteristics of the human
fingerprint.
A fingerprint image is read from a capture
device, features are extracted from the
image, and a template is created.
If appropriate precautions are followed, what
results is a very accurate means of
authentication.
Fingerprints vs Finger-scans
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Fingerprint images require 250kb per
finger for a high-quality image.
Can be acquired using ink-and-roll
procedure, optical or non-contact
methods.
Finger-scan technology doesn't store the
full fingerprint image. It stores particular
data about the fingerprint in a much
smaller template, requiring from 2501000 bytes.
AFIS
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AFIS (Automated Fingerprint Identification
Systems) - commonly referred to as "AFIS
Systems" (a redundancy) - is a term applied
to large-scale, one-to-many searches.
Although finger-scan technology can be
used in AFIS on 100,000 person databases,
it is much more frequently used for one-toone verification within 1-3 seconds.
Fingerprint Characteristics
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Can be classified
according to the
decades-old
Henry system:
left loop
 right loop
 arch
 whorl
 tented arch
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Feature Extraction Steps
Minutiae, the
discontinuities that
interrupt the otherwise
smooth flow of ridges, are
the basis for most fingerscan authentication.
Accuracy
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False Rejection Rates (FRR), or the likelihood
that the system will not "recognize" an
enrolled user's finger-scan, in the vicinity of
0.01%.
False Acceptance Rates (FAR), or the
likelihood that the system will mistakenly
"recognize" the finger-scan of a user who is
not in the system, are frequently stated in the
vicinity of 0.001%.
The point at which the FAR and FRR meet is
the Equal Error Rate, frequently claimed to be
0.1%.
Iris Scan
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Iris recognition is based on visible (via regular
and/or infrared light) qualities of the iris.
A primary visible characteristic is the trabecular
meshwork (permanently formed by the 8th
month of gestation), a tissue which gives the
appearance of dividing the iris in a radial
fashion.
Other visible characteristics include rings,
furrows, freckles, and the corona.
Iris Recognition Technology
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Iris recognition technology converts the
visible characteristics discussed before into
a 512 byte IrisCode(tm), a template stored
for future verification attempts.
Accuracy
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The odds of two different irises returning a
75% match (i.e. having a Hamming
Distance of 0.25): 1 in 10^16
Equal Error Rate (the point at which the
likelihood of a false accept and false reject
are the same): 1 in 1.2 million
The odds of 2 different irises returning
identical IrisCodes: 1 in 10^52
Benefits
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Uniqueness
Established prior to birth and
remains intact through out the life.
For more details
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Check Dr. John Daugman’s web page:
http://www.cl.cam.ac.uk/users/jgd1000
Hand Scan
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Hand-scan reads the top and sides of the
hands and fingers, using such metrics as the
height of the fingers, distance between joints,
and shape of the knuckles.
Although not the most accurate physiological
biometric, hand scan has proven to be an
ideal solution for low- to mid-security
applications where deterrence and
convenience are as much a consideration as
security and accuracy.
Example
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HandPunch
2000/3000 model
developed by
Recognition
Systems
Pros and Cons
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Advantages
Ease of use
 Resistant to
fraud
 Template size
 User perception
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Disadvantages
Static design
 Cost
 Injury to hands
 Accuracy
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Face Recognition
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Most natural because this is how
we human recognize other people.
Remains a difficult subject.
Primary Facial Scan Technologies
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Eigenfaces
feature analysis
neural network
automatic face processing
Typical Eigenfaces
Feature Analysis
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The most widely utilized facial
recognition technology
Local Feature Analysis (LFA) utilizes
dozens of features from different
regions of the face, and also
incorporates the relative location of
these features.
The extracted (very small) features
are building blocks, and both the type
of blocks and their arrangement are
used to identify/verify.
ANN Approach
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Features from both faces - the
enrollment and verification face vote on whether there is a match.
Neural networks employ an
algorithm to determine the
similarity of the unique global
features of live versus enrolled or
reference faces, using as much of
the facial image as possible.
AFP
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Automatic Face Processing (AFP)
is a more rudimentary technology,
using distances and distance ratios
between easily acquired features
such as eyes, end of nose, and
corners of mouth.
Not as robust, but AFP may be
more effective in dimly lit, frontal
image capture situations.