Face Recognition From Video

8/8/2019 Face Recognition From Video

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Face Recognition from VideoFace Recognition from Videousing Robust Kernel Resistorusing Robust Kernel Resistor--

Average DistanceAverage Distance

Prepared By :


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MotivationMotivation

SingleSingle--image face recognition has tooimage face recognition has too

many constraintsmany constraints

Video is easily obtainableVideo is easily obtainable

Video should provide more robustVideo should provide more robust

informationinformation


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Problem StatementProblem Statement

Goal: Recognition from video withoutGoal: Recognition from video without

heavy constraints on subjects movementheavy constraints on subjects movement

Problem: Unconstrained data includesProblem: Unconstrained data includes

highly nonhighly non--linear variationslinear variations

Question: How to find distinguishableQuestion: How to find distinguishable

features in disorganized data?features in disorganized data?


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Given a sequence of face images inGiven a sequence of face images in

random* positions:random* positions:

How to use data without determining realHow to use data without determining real--

world position?world position?

How to distinguish different subjects?How to distinguish different subjects?


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Given a sequence of face images inGiven a sequence of face images in

random* positions:random* positions:

How to use data without determining realHow to use data without determining real--

world position?world position?

Kernel PCA projectionKernel PCA projection

How to distinguish different subjects?How to distinguish different subjects?ResistorResistor--Average Distance measureAverage Distance measure


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General Technique toGeneral Technique to

compare subjectscompare subjects For each set of images, align the facesFor each set of images, align the faces

through affine transformationthrough affine transformation

Remove outliersRemove outliers Create synthetic data to improveCreate synthetic data to improve

robustnessrobustness

Create KCreate K--face space using both setsface space using both sets Project data sets and compare distancesProject data sets and compare distances


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PCA RefresherPCA Refresher

Principal component analysis projects dataPrincipal component analysis projects data

into a subspaceinto a subspace

Preserves distinguishing features by usingPreserves distinguishing features by usingeigenvectorseigenvectors VVof covariance matrixof covariance matrix CC

N

iixlix ! ,,...,1,

!!

l

iii

xx

l

C

1

T1

CVV !P


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Kernel PCAKernel PCA

Since PCA wont work for nonSince PCA wont work for non--linearlinear

variations, map the data to anvariations, map the data to an

approximately linear spaceapproximately linear space FF

FN

p* :


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New problem:New problem:

Solving this directly is expensiveSolving this directly is expensive

Instead, use the kernel trickInstead, use the kernel trick

! **!l

i

ii xxl 1

T

)()(1

VCV !P

(1)


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Eigenvectors will lie in the span of projectedEigenvectors will lie in the span of projected

datadata

We can use equivalent systemWe can use equivalent system

and extract the same values forand extract the same values forV.V.

)(),...,( 1 lxx **

lkVCxVxii

,...,1),)(())(( !*!*P (2)


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Further,Further, VVcan be described as a linearcan be described as a linear

combination of the datas projection.combination of the datas projection.

A key to the trick, define the kernel matrixA key to the trick, define the kernel matrix K.K.

!

*!l

i

iixV

1

)(E

))()((: T jiij xxK **!

(3)


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lkxxxl

xxx

l

i

ii

l

i

iii

l

i

iii,...,1),)()()(

1)(())()((

11

T

1

!****!** !!!

EEP

EEP2

KKl !

Substituting (1) and (3) into (2), we get:

Now, using the kernel matrix we get:

EPE Kl !

Which will have equivalent solutions to theeasily solved:


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The trick to Kernal PCAThe trick to Kernal PCA

We can easily have our subspace, but how toWe can easily have our subspace, but how to

constructconstruct KK??

Consider a mapping with polynomial degree 5,Consider a mapping with polynomial degree 5,and 16x16 pixel imagesand 16x16 pixel images

It would require 10It would require 101010dimensionality!dimensionality!

Instead, just define a kernel functionInstead, just define a kernel function

))()((),( T yxyxk **!


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Kernel functionKernel function

Using the kernel function to build the kernelUsing the kernel function to build the kernel

matrix will make computation reasonablematrix will make computation reasonable

Arandjelovic and Cipolla usedArandjelovic and Cipolla used

found empiricallyfound empirically

)()(6. T),( yxyxeyxk !


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Summary of algorithmSummary of algorithm

For2 input sets, automatically detectFor2 input sets, automatically detect

eyes and nostrils & perform affineeyes and nostrils & perform affine

transform to normalizetransform to normalize


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ResisterResister--Average DistanceAverage Distance

RAD works as a measure because unlikeRAD works as a measure because unlike

KLD, it is symmetricKLD, it is symmetric


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Using RANSAC, keep only inliersUsing RANSAC, keep only inliers

Create additional synthetic data for eachCreate additional synthetic data for each

set by applying slight, randomset by applying slight, random

perturbations to input imagesperturbations to input images


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Apply RANSAC Kernel PCA to the unionApply RANSAC Kernel PCA to the union

of augmented datasetsof augmented datasets

Randomly select samples from dataRandomly select samples from data Compute KPCA for samplesCompute KPCA for samples

Project data into KProject data into K--face space, count howface space, count how

many data {zmany data {zii} lie within threshold of origin} lie within threshold of origin

RepeatRepeat

Use the largest {zUse the largest {zii} to create K} to create K--spacespace


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Separately, project each augmented setSeparately, project each augmented set

into Kinto K--face space.face space.

Use the set of projections as a probabilityUse the set of projections as a probabilitydistribution model for each subjectdistribution model for each subject

Because of the nonlinear projection, theseBecause of the nonlinear projection, these

distributions should be normaldistributions should be normal

Use RAD to measure distancesUse RAD to measure distances


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Experimental ResultsExperimental Results


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Experimental ResultsExperimental Results

Training consisted of30Training consisted of30--50 video images (taken50 video images (taken

at 10fps); testing consisted of sets of35at 10fps); testing consisted of sets of35

At best, achieved 98% recognition with 2% errorAt best, achieved 98% recognition with 2% error


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ReferencesReferences

O. Arandjelovic and R. Cipolla. Face Recognition from Face MotionO. Arandjelovic and R. Cipolla. Face Recognition from Face Motion

Manifolds using Robust Kernel ResistorManifolds using Robust Kernel Resistor--Average Distance.Average Distance. FaceFace

Processing in VideoProcessing in Video, 2004, 2004

D. H. Johnson and S. Sinanovic. Symmetrizing the kullbackleiblerD. H. Johnson and S. Sinanovic. Symmetrizing the kullbackleibler

distance.distance. Rice University Working PaperRice University Working Paper, 2001., 2001.

B. Scholkopf, A. Smola, and K. Muller. Kernel principal componentB. Scholkopf, A. Smola, and K. Muller. Kernel principal component

analysis.analysis. Advances in Kernel MethodsAdvances in Kernel Methods SVLearningSVLearning, pages 327, pages 327352,352,

1999.1999.

Face Recognition From Video

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