Intro to Biometrics

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17 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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Biometrics

W. A. Barrett, cmpe dept., SJSU

vs. 2.0


Theory in a nutshell


Segmentation


Recognition


Verification


Fingerprints/Face/Iris/Speaker recognition


Logface matching

Theory in a Nutshell


Capture
images of objects (usually persons)


Segment a view


Compress views to
biometric codes.


Compare two biometric codes, yielding a
biometric difference.


When two differences are small enough (less
than some threshold), the corresponding
objects are considered the same.


Otherwise the objects are considered
different.

A Sample Database

distance j

=


square
(D[j,i]
-

C[i])/var[i]

where

D[j,i] = database j’s component i, 1 <= j <= 5 (rows)

C[i] = candidate component i, 1 <= i <= 6 (columns)

var[i]= variance from database, component i

NAME
BIOCODE
Bill Barrett
5.9.6.30.7.6
Dave Matthews
3.9.4.25.9.7
Mike Sanders
5.8.4.33.6.5
Fred Friendly
8.4.2.28.7.3
Bill Clinton
2.6.3.30.6.7
Distance Calculation

(see spreadsheet
local.xls

or
web.xls

NAME
BIOCODE
B I O C O D E S
Bill Barrett
5.9.6.30.7.6
5
9
6
30
7
6
Dave Matthews
3.9.4.25.9.7
3
9
4
25
9
7
Mike Sanders
5.8.4.33.6.5
5
8
4
33
6
5
Fred Friendly
8.4.2.28.7.3
8
4
2
28
7
3
Bill Clinton
2.6.3.30.6.7
2
6
3
30
6
7
AVERAGE
4.6
7.2
3.8
29.2
7
5.6
VARIANCE
5.3
4.7
2.2
8.7
1.5
2.8
Candidate:
4.8
7.9
3.2
31
6.2
5.1
DISTANCE
D I S T A N C E S
Bill Barrett
4.66
0.01
0.26
3.56
0.11
0.43
0.29
Dave Matthews
11.81
0.61
0.26
0.29
4.14
5.23
1.29
Mike Sanders
0.79
0.01
0
0.29
0.46
0.03
0
Fred Friendly
8.86
1.93
3.24
0.65
1.03
0.43
1.58
Bill Clinton
3.7
1.48
0.77
0.02
0.11
0.03
1.29
Segmentation


Image typically contains background noise


Segmentation

is isolating a biometric view
from the image


Motion segmentation uses video to reject static
background pixels


Two or more cameras yield distance measures


Given a static image, segmentation requires
heuristic methods


Static segmentation may be the most difficult
design challenge of a biometric system

Recognition


Form an
enrolled

database

of biometric codes


each entry represents a different
candidate


each candidate is associated with a biometric
code, name, address, etc.


Capture a view of a candidate and compute its
biometric code
C.


Compare
C with all candidates in the database.


Form a list of database candidates, ordered by
increasing biometric distance.


Front of the list should be the matching candidates.

Recognition (2)


If the top candidate has a small
-
enough
biometric
distance
, we say that we have
recognized

the candidate.


If the top candidate's biometric distance is
too large, then the candidate has
not

been
recognized.


This implies a
threshold level

has been
determined for biometric differences

Recognition
--

Four Cases


(good) Top candidate's biocode is small
enough, and is the
correct

person.


(bad) Top candidate's biocode is small
enough, but is the
wrong

person (false
acceptance)


(good) Top candidate's biocode is too large,
and this is the
wrong

person.


(bad) Top candidate's biocode is too large,
yet this is the
correct

person (false rejection)

Recognition Goals


Maximize

correct matching of a candidate to
the database


Minimize

false acceptance and false
recognition

Verification


Candidate presents biometric image PLUS
identification information, such as a credit
card plus PIN


System locates candidate in the database
through the credit card/PIN data


One biometric distance is computed
--

if
small enough, the candidate is verified.


Can still have a false acceptance or false
rejection!

Authentics
-
Imposters


Biometric quality is measured statistically by
acquiring two distributions
--


Authentics

--

distribution of biometric
distances of the
same

persons, but with
different

images


Imposters

--

distribution of biometric
distances of images of pairs of
different

persons


These should be widely separated, but often
aren't

Authentics
-

Imposters

Authentics
-
Imposters


The two distributions will
overlap

in general


The extent of the overlap relative to the two areas
provides a measure of the quality of this biometric
measure


Small overlap
--

good biometric


Large overlap
--

poor biometric


Best viewed through the
accumulated

distribution


shows probability of correct identification


See spreadsheet
local.xls
or

web.xls

for a model

Authentics
-
Imposters

Choice of Threshold


At the crossover of the A
-
I curves, we have a
threshold that makes

false acceptance rate == false rejection rate


Assumes that the relative number of attempts
is balanced


Moving the threshold to the
left

means more
false rejections, but fewer false acceptances


Moving the threshold to the
right

means
fewer false rejections, more false
acceptances

Quality Measure


The quality of a biometric measure can be
estimated from these two curves


use a good representative sample of
measurements (not easily done!)


find the crossover point


FARR = % at crossover point


FARR: False Acceptance
-
Recognition Ratio

View Compression


Task: form a
biometric code

from a
view


Fast Fourier transform


Gabor wavelet transform


Legendre moments


Chebyshev moments


pseudo
-
Zernike moments


The choice should:


eliminate unwanted view variations (scale,
rotation, translation, avg intensity, etc.)


produce maximum discrimination, i.e. smallest
possible FARR

Legendre Moments

f(x,y) is the image intensity vector

P
0
(x) = 1, P
1
(x) = x












1
1
1
1
)
,
(
)
(
)
(
4
)
1
2
)(
1
2
(
dy
dx
y
x
f
y
P
x
P
q
p
L
q
p
pq



n
x
P
n
x
P
x
n
x
P
n
n
n
)
(
)
1
(
)
(
)
1
2
(
)
(
2
1






Legendre Moments


Are orthogonal and complete


the view can be reconstructed, given enough
(p,q) pairs


Are translation invariant


the translation component is in (0,0)


Are
not

scale invariant


face: need to rescale to a normal view, typically
done by finding the eyes, etc.


Are
not

rotation invariant


face: measure degrades with rotation

pseudo
-
Zernike Moments


Much more complex set of polynomials


Are orthogonal and complete


Not scale or translation invariant


Certain functions of the moments are rotation
invariant


most human biometrics don't need this


Used in advanced optical calculations


Useful for logface biometrics

Face Recognition


Many methods have been proposed


eigenfaces (Alex Pentland, MIT)


feature extraction (Joseph Attick, Identix)


some are proprietary


Discrimination depends critically on


uniform lighting conditions


full frontal face
--

no side views


“plain” expression


no attempt at disguise


good segmentation, centering the eyes


Best results FARR = 1
-
5%

Face Recognition


Relatively high FARR means restricted use:


verification under controlled conditions (disguise
can be used to evade detection, but difficult to
fake a verification trial)


sifting out a small number of candidates from a
larger set


NOT indicated for


recognition


critical applications

Fingerprints


For digital prints, the FBI routinely finds
persons in their large national database from
prints sent through the internet (AFIS)


Statistics are unknown, but believed to have
a FARR less than 1
E
-
5


Fingerprint analysis for forensic purposes has a
much smaller FARR


Small or smudged prints (typical of crime
scenes) are likely to result in identification errors.

Iris Scanning

Iris Scanning


Image capture requires telephoto camera


Daugman recommends infrared light


Locate pupil (heuristic)


Daugman uses a circle
-
finding algorithm


Locate sclera


surrounds pupil


Locate upper and lower eyelids


Form biocode from iris patterns


Daugman uses 8 bands and a Gabor filtering to
yield a 256 byte code


Distance measure


Daugman uses a Hamming distance measure

Iris Scan A
-
I distribution

from John Daugman's patent

Iris Issues


Pupil finding is difficult


Background light sources reflected in pupil


Eyelashes sometimes obscure iris


Eyes may be partly closed


Eye movements are rapid, may cause image
capture failure


Telephoto centering and autofocus important


Capture system can be expensive


Sensar’s manufacturing cost ~$2,000


Recognition failure rate fairly large ~1
-
5%

Sensar, Inc.


A New Jersey startup, 1990
-
2000 period


Used the Daugman iris patent


Developed extraordinary optics system


two cameras, one wide
-
angle, the other a
telephoto with autofocus and angular tracking


system could accurately identify a person as
he/she approached an ATM machine


tested in a Fort Worth bank system


Sensar failed for various reasons

Speaker Recognition


Starts with an audio sample of a human
voice


Typically, person is prompted to repeat
certain phrases


Speech fragment compressed by FFT or
wavelet transforms


Identification/verification similar to other
biometrics


FARR ~ 1E
-
2 at best

Forest Service Project


Goal
--

Match a cut log face to its mating
stump


U. S. Forest Service interested in combating
theft of timber from national forests


start with photo of stump face


find stump face in a collection of photographs of
faces taken at various sawmills


use biometrics to filter out the most likely
candidates


use forensic tools to indict and prosecute thieves

Logface System Features


Color images input by digital camera, many
supported image formats


Semi
-
automatic segmentation of log faces


operator segmentation needed


Uses pseudo
-
Zernike polynomials to obtain a
rotation
-
invariant biometric code


Database
mysql

employed under Linux


Friendly user environment for locating
matching faces from a database

Logface Results

Selected Bibliography

http://www.biometrics.org

--

Biometrics web site

http://www.identix.com

--

Face recognition, fingerprint vendor

http://www.iritech.com

--

Daugman’s iris scanning company, patent holder

John Daugman, patent no. 5,291,560, Iris scanning patent

Wechsler
et al,
editors,
Face Recognition

Maltoni, Maio, Jain & Prabhakar,
Handbook of Fingerprint Recognition,


Springer, 2003.

Mukundan & Ramakrishnan,
Moment Functions in Image Analysis,


World Scientific, 2003

Duda & Hart,
Pattern Classification and Scene Analysis, Wiley Interscience

Fukunaga,
Introduction to Statistical Pattern Recognition, Academic Press

Theodoridis & Koutroumbas,
Pattern Recognition, Academic Press

Summary


Biometrics is an established discipline


...though research is ongoing


Mechanism is


compressing an image into a biocode


comparing pairs of biocodes with a distance
measure
d(I1, I2)


forming a database of enrollees


locating or verifying a candidate against the
database with the distance measure

Summary


FARR = equal false acceptance and false
rejection ratio


Most popular human biometrics


digital fingerprints, with FARR ~ 1E
-
5


forensic fingerprints (non
-
digital), FARR < 1E
-
7


face, with FARR ~ 1E
-
2 at best


iris, with FARR < 1E
-
7


speaker recognition, with FARR < 1E
-
2


Other applications


Draws upon pattern recognition theory