face recognition - biometrics

8/16/2019 Face Recognition - Biometrics

1/42

Face Recognition


2/42


3/42

echnically a three-step procedure +#

• Sensor – ! ta'es observation) ! develops biometric signature. Eg) Camera)

• Normalization

! same format as signature in database)

! develops normalize! signature) Eg) Shape alignment, intensity correction

• "atcher ! compares normalized signature -ith the set of normalized

signature in system database) ! gives similarity score or !istance measure) Eg) .ayesian techni/ue for matching


4/42

#onsi!erations for a potential Face

Recognition System

*ode of operation Size of database for identification or -atch list 0emographics of anticipated users) 1ighting conditions) System installed overtly or covertly User behavior 2o- long since last image enrolled 3e/uired throughput rate *inimum accuracy re/uirements


5/42

$rimary Facial Scan Technologies

%. &igenfaces 'one(s own face)

! Utilizes the t-o dimensional global grayscale imagesrepresenting distinctive characteristics)

*. Feature +nalysis ! accommodates changes in appearance or facial aspect.

,. Neural Networs

! features from enrollment and verification face vote on match)

() +utomatic Face $rocessing

! uses distance and distance ratios ! used in dimly lit, frontal image capture)


6/42

Sensors

Used for image capture Standard off#the#shelf 4C cameras, -ebcams) 3e/uirements+

! Sufficient processor speed 5main factor6 ! Ade/uate "ideo card)

! 789 : 8(9 resolution)

! 7#; frames per second)

5 more frames per second and higher resolution lead to abetter performance)6•


7/42

FaceCam

0eveloped by "isionSphere) Face recognition technology

integrated -ith speechrecognition in one device)

Features User#friendly) Cost#effective) >on#intrusive) Auto#enrollment Auto#

location of user) "oice prompting) &mmediate user feedbac')


8/42

#omponents of Face#am

• &ntegrated Camera• 1C0 0isplay 4anel• Alpha#>umeric 'eypad• Spea'er, *icrophone• Attached to 4entium && class &.* compatible 4C

5containing an >SC capture card and "isionSphere?s face

recognition soft-are6+!vantages of Face#am

• 1iveness test is performed)• False Accept rate and False 3e@ect 3ate is approximately

$)Other sensors• A("ision technology#uses structured light in near#infrared

range)• 4a4e3o 5>EC?s $artner#type $ersonal Robot6


9/42

Feature &traction

/imensionality Re!uction Transforms Barhunen#1oeve ransform%Expansion 4rincipal Component Analysis Singular "alue 0ecomposition 1inear 0iscriminant Analysis Fisher 0iscriminant Analysis &ndependent 0iscriminant analysis

/iscrete #osine transform

0abor 1aveletSpectrofaces

Fractal image co!ing


10/42

/imensionality Re!uction Transforms

2arhunuen-3oeve Transform he 23 Transform operates a dimensionality reduction on the

basis of a statistical analysis of the set of images from theircovariance matrix)

&igenvectors and the &igen4alues of the covariance matrixare calculated and only only the eigenvectors corresponding tothe largest eigenvalues are retained i)e) those in -hich theimages present the higher variance)


11/42

$rincipal #omponent +nalysis

Each spectrum in the calibration set -ould have a different set ofscaling constants for each variation since the concentrations ofthe constituents are all different) herefore, the fraction of eachspectrum that must be added to reconstruct the un'no-n datashould be related to the concentration of the constituents

he variation spectra are often called eigenvectors 5a)')a),spectral loadings, loading vectors, principal components orfactors6, for the methods used to calculate them) he scalingconstants used to reconstruct the spectra are generally 'no-nas scores) his method of brea'ing do-n a set spectroscopicdata into its most basic variations is called $rincipal

#omponents +nalysis 5$#+6) 4CA brea's apart the spectral data into the most common

spectral variations 5factors, eigenvectors, loadings6 and thecorresponding scaling coefficients 5scores6)


12/42


13/42


14/42

/iscrete #osine Transform

/#T is a transform used to compress therepresentation of the data by discarding redundantinformation)

Adopted by D4E

+nalogous to Fourier Transform, 0C transformssignals or images from the spatial domain to thefre/uency domain by means of sinusoidal basisfunctions, only that 0C adopts real sine functions)

0C basis are in!epen!ent on the set of images)

0C is not applied on the entire image, but is ta'enfrom s/uare#sampling -indo-s)


15/42

/iscrete #osine Transform


16/42

0abor 1avelet

he preprocessing of images by abor -avelets is chosen forits biological relevance and technical properties)

he abor -avelets are of similar shape as the receptivefields of simple cells in the primary visual cortex)

hey are localized in both space and fre/uency domains andhave the shape of plane -aves restricted by a aussianenvelope function)

Capture properties of spatial localization, orientationselectivity, spatial fre/uency selectivity and /uadrature phaserelationship)

A simple model for the responses of simple cells in theprimary visual cortex) &t extracts edge and shape information) &t can represent face image in a very compact -ay)


17/42

0abor 1avelet


18/42

0abor 1avelet

3eal 4art &maginary 4art


19/42

0abor 1avelet

Advantages+ Fast

Acceptable accuracy

Small training set

0isadvantages+ Affected by complex bac'ground

Slightly rotation invariance


20/42

SpectroFace

Face representation method using -avelet transformand Fourier ransform and has been proved to beinvariant to translation, on#the#plane rotation and scale)

First order Second order

he first order spectroface extracts features, -hich aretranslation invariant and insensitive to facial expressions,small occlusions and minor pose changes)

Second order spectroface extracts features that areinvariant to on#the#plane rotation and scale)


21/42

SpectroFace


22/42

Fractal image #o!ing

An arbitrary image is encoded into aset of transformations, usually affine)&n order to obtain a fractal model of aface image, the image is partitionedinto non#overlapping smaller bloc's

5range6 and overlapping bloc's5domain6) A domain pool is preparedfrom the available domain bloc's)For each range bloc', a search isdone through the domain pool to finda domain bloc' -hose contactive

information best approximates therange bloc') A distance metric suchas 3*S can find the approximationerror)


23/42

Fractal 8mage #o!ing

*ain Characteristic3elies on the assumption that image redundancy can

be efficiently captured and exploited through

piece-ise self#transformability on a bloc'#-ise basis,and that it approximates an original image -ith thefractal image, obtained from a finite number ofiterations of an image transformation called fractalcode)


24/42

/ata +c9uisition problems

8llumination

$ose 4ariation

&motion


25/42

8llumination problem in face recognition

"ariability in&llumination

Contrast *odel


26/42

+pproaches to counter illumination

problem euristic +pproaches

0iscards the three most significant components Assumes that the first fe- principal components capture only variation

in lighting 8mage #omparison +pproaches

Uses image representations such as edge maps, derivatives ofgraylevel, images filtered -ith 80 gabor li'e functions and arepresentation that combines a log function of the intensity to theserepresentations)

.ased on the observation that the difference bet-een the t-o images ofthe same ob@ect is smaller than the difference bet-een images ofdifferent ob@ects)

Extracts 0istance measures such as• 4oint -ise distance

• 3egional distance• Affine#1 distance 1ocal Affine#1 distance 1og point-ise distance


27/42

#lass-base! +pproaches3e/uires three aligned training images ac/uired under different lighting

conditions)Bohonen?s S


28/42

$ose $roblem in Face Recognition

4erformance of biometric systems drops significantly -henpose variations are present in the image)

3otation problem *ethods of handling the rotation problem

"ulti-image base! approaches *ultiple images of each person is used

ybri! +pproaches

*ultiple images are used during training, butonly one database image per person is usedduring recognition

Single 8mage base! approaches

>o pose training is carried out


29/42

"ulti-8mage base! approaches

Uses a emplate#base correlation matching scheme) For each hypothesized pose, the input image is aligned to

database images corresponding to that pose) he alignment is carried out via a 80 affine transformation

based on three 'ey feature points Finally, correlation scores of all pairs of matching templates

are used for recognition) 3imitations

*any different vie-s per person are needed in thedatabase

>o lighting variations or facial expressions are allo-ed2igh computational cost due to iterative searching)


30/42

ybri! +pproaches

*ost successful and practical

*a'e use of prior class information

"etho!s

1inear class#based methodraph#matching based method

"ie-#based eigenface method


31/42

Single-8mage ;ase! +pproaches

&ncludes1o-#level feature#based methods

&nvariant feature based methods

70 model based methods


32/42

"atching Schemes

>earest >eighbor

>eural >et-or's

0eformable *odels 2idden *ar'ov *odels

Support "ector *achines


33/42

Nearest Neighbor

A nave >earest >eighbor classifier is usuallyemployed in the approaches that adopt adimensionality reduction techni/ue) Extract the most representative%discriminant featuresby pro@ecting the images of the training set in anappropriate subspace of the original space 3epresent each training image as a vector of -eightsobtained by the pro@ection operation

3epresent the test image also by the vectors of-eights, then compare these vectors to the trainingimages in the reduced space to determine -hich class itbelongs


34/42

Neural Networs

+ NN approach to 0en!er #lassification: Using vectors of numerical attributes, such as eyebro-thic'ness, -idths of nose and mouth, chin radius, etc -o 2yper.F net-or's -ere trained for each gender .y extending feature vectors, and training one 2yper.Ffor each person, this system can be extended to performface recognition

+ fully automatic face recognition system base! on

$robabilistic /ecision-;ase! NN 5$/;NN6: A hierarchical modular structure 0.>> and 1US learning


35/42

Neural Networs - #ont

+ hybri! NN solution

Combining local image sampling, a Self# and a convolutional >>

S> provides for partial invariance totranslation, rotation, scale, and deformation


36/42

Neural Networks - Cont

A system based on Dynamic Link Architecture (DLA) DLAs use synaptic plasticity and are able to instantly form setsof neurons grouped into structured graphs and maintain theadvantages of neural systems

Gabor based wavelets for the features are used The structure of signal is determined by 3 factors: input image,random spontaneous ecitation of the neurons, and interactionwith the cells of the same or neighboring nodes

!inding between neurons is encoded in the form of temporal

correlation and is induced by the ecitatory connections withinthe image


37/42

Deformable Models

Templates are allowed to translate, rotate and deform to

fit the best representation of the shape present in image

"mploy wavelet decomposition of the face image as #ey

element of matching pursuit filters to find the subtledifferences between faces

"lastic graph approach, based on the discrete wavelet

transform: a set of Gabor wavelets is applied at a set of

hand$selected prominent ob%ect points, so that each point isrepresented by a set of filter responses, named as a &et


38/42

Hidden Markov Models

Many variations of HMM have been introduced forface reconition !roblem" Luminance$based 'D$()) D*T$based 'D$()) +D seudo ()) "mbedded ()) Low$*ompleity +D ())

(ybrid ())#bservable features of these systems are either rawvalues of the !i$els in the scannin element ortransformation of these values


39/42

%u!!ort &ector Machines

!eing maimum margin classifiers, -.) aredesigned to solve two$class problems, while facerecognition is a /$classes problem, / 0 number of#nown individuals

'wo a!!roaches" 1eformulate the face recognition problem as a

two$class problem "mploy a set of -.)s to solve a generic /$classes recognition problem


40/42

Advantaes of ace econition %ystems

Non-intrusive *

2ther biometrics re/uire sub%ect co$operation and

awareness eg 4ris recognition 5loo#ing into eye scanner

lacing hand on fingerprint reader

!iometric data readable and can be verified by a human

6o association with crime


41/42

A!!lications for ace econition

'echnoloy

+overnment ,se Law "nforcement *ounter Terrorism 4mmigration

Legislature

Commercial ,se Day *are Gaming 4ndustry

1esidential -ecurity "$*ommerce .oter .erification !an#ing


42/42

%tate of the art

Three protocols for system evaluation are '. /M0&'%and &'

*ommercial applications of 71T include face verification basedA'M and access control and Law enforcement applicationsinclude video surveillance

!oth lobal 8based on 9L epansion and local 8domain#nowledge 5face shape, eyes, nose etc face descriptors areuseful

#!en esearch 1roblems

6o general solutions for variations in face images li#eillumination and !ose !roblems

roblem of ain ;;;

face recognition - biometrics

Documents