Biometrics

utterlypanoramicSecurity

Nov 30, 2013 (3 years and 4 months ago)

44 views

Pattern Recognition

1/
6/2009

Instructor:

Wen
-
Hung Liao, Ph.D.

Biometrics

Outline


Basic Concepts


Fingerprint



Iris Scan


Hand Geometry


Face Recognition

Identification vs Verification


Identification: Who am I? One
-
to
-
many
search


Verification: Am I who I claim I am? One
-
to
-
one search


Detection: Find out whether there is an
instance of a given type of object in an
environment.


Recognition: detection + identification


Terminology


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


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?


Uniqueness


Invariance


Non
-
intrusive


Easy (or not too difficult) to acquire


Low processing cost



Fingerprint


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


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 250
-
1000 bytes.


AFIS


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
-
to
-
one verification within 1
-
3 seconds.


Fingerprint Characteristics


Can be classified
according to the
decades
-
old
Henry system:


left loop


right loop


arch


whorl


tented arch

Feature Extraction Steps



Minutiae, the
discontinuities that
interrupt the otherwise
smooth flow of ridges, are
the basis for most finger
-
scan authentication.

Accuracy


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


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


Iris recognition technology converts the
visible characteristics discussed before into
a 512 byte IrisCode(tm), a template stored
for future verification attempts.

Accuracy


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


Uniqueness


Established prior to birth and
remains intact through out the life.

For more details


Check Dr. John Daugman’s web page:


http://www.cl.cam.ac.uk/users/jgd1000


Hand Scan


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


HandPunch
2000/3000 model
developed by
Recognition
Systems

Pros and Cons


Advantages


Ease of use


Resistant to
fraud


Template size


User perception




Disadvantages


Static design



Cost


Injury to hands


Accuracy

Face Recognition


Most natural because this is how
we human recognize other people.


Remains a difficult subject.

Primary Facial Scan Technologies


Eigenfaces


feature analysis


neural network


automatic face processing

Typical Eigenfaces

Feature Analysis


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


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


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.