Biometrics

W. A. Barrett, cmpe dept., SJSUvs. 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]whereD[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 BIOCODEBill Barrett 5.9.6.30.7.6Dave Matthews 3.9.4.25.9.7Mike Sanders 5.8.4.33.6.5Fred Friendly 8.4.2.28.7.3Bill 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 SBill Barrett 5.9.6.30.7.6 5 9 6 30 7 6Dave Matthews 3.9.4.25.9.7 3 9 4 25 9 7Mike Sanders 5.8.4.33.6.5 5 8 4 33 6 5Fred Friendly 8.4.2.28.7.3 8 4 2 28 7 3Bill Clinton 2.6.3.30.6.7 2 6 3 30 6 7

AVERAGE 4.6 7.2 3.8 29.2 7 5.6VARIANCE 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 SBill Barrett 4.66 0.01 0.26 3.56 0.11 0.43 0.29Dave Matthews 11.81 0.61 0.26 0.29 4.14 5.23 1.29Mike Sanders 0.79 0.01 0 0.29 0.46 0.03 0Fred Friendly 8.86 1.93 3.24 0.65 1.03 0.43 1.58Bill 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

P0(x) = 1, P1(x) = x

1

1

1

1

),()()(4

)12)(12(dydxyxfyPxP

qpL qppq

n

xPnxPxnxP nn

n

)()1()()12()( 21

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 1E-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 sitehttp://www.identix.com -- Face recognition, fingerprint vendorhttp://www.iritech.com -- Daugman’s iris scanning company, patent holderJohn Daugman, patent no. 5,291,560, Iris scanning patentWechsler et al, editors, Face RecognitionMaltoni, Maio, Jain & Prabhakar, Handbook of Fingerprint Recognition, Springer, 2003.Mukundan & Ramakrishnan, Moment Functions in Image Analysis, World Scientific, 2003Duda & Hart, Pattern Classification and Scene Analysis, Wiley InterscienceFukunaga, Introduction to Statistical Pattern Recognition, Academic PressTheodoridis & 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