f ace r ecognition e igen-faces with 99 pca coefficients project presentation ashish tiwari...

FFace ace RRecognitionecognitionEEigen-faces with 99 PCA igen-faces with 99 PCA

coefficientscoefficients

Project PresentationProject PresentationAshish TiwariAshish Tiwari

MAS622J/1.126JMAS622J/1.126JPattern Classification and AnalysisPattern Classification and Analysis

Problem StatementProblem Statement

• GivenGiven• 20002000 Training data points Training data points• 20002000 Test data points Test data points• faceDRfaceDR and and faceDSfaceDS: :

Description files for both Description files for both training and testing data training and testing data points.points.

• faceRfaceR: Matlab data file : Matlab data file containing 99 PCA containing 99 PCA coefficients for training coefficients for training imagesimages

• faceSfaceS: Matlab file : Matlab file containing 99 PCA containing 99 PCA coefficients for test coefficients for test images. images.

• Required to classifyRequired to classify

GenderGender MaleMale FemaleFemale

AgeAge ChilChildd

TeeTeenn

AduAdultlt

SeniSenioror

RacRacee

White /Hispanic/ Asian/ Black/ White /Hispanic/ Asian/ Black/ OtherOther

Facial Facial ExpressioExpressionsns

SerioSeriousus

FunFunnyny

SmilinSmilingg

HatHat YesYes NoNo

MoustachMoustachee

YesYes NoNo

GlassesGlasses YesYes NoNo

BandanaBandana YesYes NoNo

ObjectiveObjective

• The Objective is to implement, test and The Objective is to implement, test and compare performance of the various ‘ compare performance of the various ‘ Pattern Classification Algorithms ’ on Pattern Classification Algorithms ’ on

different facial features and to come up different facial features and to come up with a quantitative analysis of with a quantitative analysis of

effectiveness of Algorithms in different effectiveness of Algorithms in different scenarios.scenarios.

• To develop insight of a designer’s To develop insight of a designer’s perspective.perspective.

Summary of the WorkSummary of the Work

Parzen WindowParzen Window

K- nearest neighborhoodK- nearest neighborhood

Generalized linear discriminantGeneralized linear discriminant

Neural networkNeural network

BoostingBoosting (neural nets as component (neural nets as component classifiers)classifiers)

I have implemented all 5 Algorithms for almost I have implemented all 5 Algorithms for almost all feature classification excluding Facial all feature classification excluding Facial

Expressions.Expressions.

Bad Data IssuesBad Data Issues

As it invariably happens with any real-As it invariably happens with any real-world problem data base, this problem world problem data base, this problem also contains few bad faces, missing also contains few bad faces, missing

faces and outliers.faces and outliers.

In the implementation here the faces In the implementation here the faces with missing descriptors are taken out with missing descriptors are taken out from the data base where as outliers from the data base where as outliers

and bad faces are kept as it is.and bad faces are kept as it is.

Eigen-facesEigen-facesAn IntroductionAn Introduction

• Developed in 1991 by Developed in 1991 by M.TurkM.Turk• Based on PCABased on PCA

• Relatively simpleRelatively simple• FastFast

&&• RobustRobust

EigenfacesEigenfaces• PCA seeks directions that are efficient PCA seeks directions that are efficient

for representing the datafor representing the data• PCA reduces the dimension of the dataPCA reduces the dimension of the data• Speeds up the computational timeSpeeds up the computational time

efficient

not effic

ient

Class A

Class B

Class A

Class B

Eigenfaces, the Eigenfaces, the algorithmalgorithm

• Original ImagesOriginal Images

2

1

2

N

h

h

h

2

1

2

N

b

b

b

2

1

2

N

a

a

a

……


• The mean face The mean face can be can be computed as:computed as:

Mean-FaceMean-Face

2 2 2

1 1 1

2 2 21, 8

N N N

a b h

a b hm where M

M

a b h


• Then subtract it from the training Then subtract it from the training facesfaces

2 2 2 2 2 2 2 2

2 2

1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2

1 1 1 1

2 2

, , , ,

,

m m m m

N N N N N N N N

m m

N N

a m b m c m d m

a m b m c m d ma b c d

a m b m c m d m

e m f m

e m fe f

e m

2 2 2 2 2 2

1 1 1 1

2 2 2 2 2 2, ,m m

N N N N N N

g m h m

m g m h mg h

f m g m h m


• Now we build the matrix which is Now we build the matrix which is NN22

by by MM

• The covariance matrix which is The covariance matrix which is NN22 by by NN22

m m m m m m m mA a b c d e f g h

Cov AA


• Find eigenvalues of the covariance matrixFind eigenvalues of the covariance matrix– The matrix is very largeThe matrix is very large– The computational effort is very bigThe computational effort is very big

• We are interested in at most We are interested in at most M (?)M (?) eigenvalueseigenvalues– And in the given problem we have taken 99 And in the given problem we have taken 99

most significant Eigen directions to most significant Eigen directions to represents the data.represents the data.

Results – Results – Gender ClassificationGender Classification

KNNKNN MaleMale FemaleFemale AccurAccuracyacy

MaleMale 917917 360360 71.871.8%%

FemalFemalee

297297 422422 58.658.6%%

Overall Accuracy: Overall Accuracy:

66.95%66.95%

NNETNNET MaleMale FemaleFemale AccuraAccuracycy

MaleMale 959/12959/127777

318/7318/71919

75.0975.09%%

FemaFemalele

304/12304/127777

415/7415/71919

57.7157.71%%


63.15%63.15%BOOSTINGBOOSTING MaleMale FemaleFemale AccuraAccuracycy

MaleMale 920920 357357 72.0472.04%%

FemaleFemale 279279 440440 61.1961.19%%


68%68%

ParzeParzenn

MaleMale FemaleFemale AccuraAccuracycy

MaleMale 12761276 11 99.9299.92%%

FemaleFemale 00 719719 100%100%Overall Accuracy: Overall Accuracy:

99.75%99.75%

Results – Results – Gender ClassificationGender Classification

GeneralizGeneralized Linear ed Linear DiscriminDiscrimin

antant

MaleMale FemaleFemale AccuraAccuracycy

MaleMale 10241024 253253 80.18%80.18%

FemaleFemale 267267 452452 62.86%62.86%

Overall Accuracy: Overall Accuracy: 73.94% 73.94%

Discussion – Discussion – Gender ClassificationGender Classification

Male / FemaleMale / Female

• Very well behaved data Very well behaved data set.set.

• Both class examples are in Both class examples are in good proportion.good proportion.

• Good results are obtained Good results are obtained using using Generalized linear Generalized linear

discriminantdiscriminant..• Even comparable results Even comparable results

for simplest “Minimum for simplest “Minimum distance Classifier”distance Classifier”

• NeuralNeural netnet, , GLDGLD and and KNNKNN results are almost same.results are almost same.

• Wonderful performance by Wonderful performance by Parzen windowParzen window classifier. classifier.

Boosting – Boosting – commentscomments

Boosting doesn’t seem Boosting doesn’t seem improving the improving the

performance anymore for performance anymore for this class.this class.

• Very few training points.Very few training points.

• Samples are not in Samples are not in equal proportion in equal proportion in

second dataset (D2) second dataset (D2) (not proper utilization of the (not proper utilization of the

entire training set)entire training set)

Results – Results – Race ClassificationRace Classification

KNNKNN Correctly Correctly ClassifiedClassified

AccuraAccuracycy

WhiteWhite 1657/16991657/1699 97.5297.52%%

HispaniHispanicc

0/130/13 0%0%

AsianAsian 1/261/26 3.843.84%%

BlackBlack 9/2499/249 0.030.03%%

OtherOther 0/90/9 0%0%


ParzenParzen Correctly Correctly ClassifiedClassified

AccuraAccuracycy

WhiteWhite 1699/16991699/1699 100%100%

HispanHispanicic

0/130/13 00

AsianAsian 0/260/26 00

BlackBlack 0/2490/249 00

OtherOther 0/90/9 00


Results – Results – Race ClassificationRace Classification

NNETNNET Correctly Correctly ClassifiedClassified

AccuraAccuracycy

WhiteWhite 1476/16991476/1699 86.8786.87%%

HispanHispanicic

0/130/13 0%0%

AsianAsian 1/261/26 3.843.84%%

BlackBlack 15/24915/249 5.795.79%%

OtherOther 1/91/9 7.697.69%%


BOOSTINBOOSTINGG

CorrectCorrect AccuraAccuracycy

WhiteWhite 1322/161322/169999

77.8177.81%%

HispanicHispanic 3/133/13 23.0723.07%%

AsianAsian 9/269/26 34.6134.61%%

BlackBlack 12/24912/249 4.634.63%%

OtherOther 8/98/9 88.8388.83%%

Overall Accuracy: Overall Accuracy: 67.95%67.95%

Discussion- Discussion- Race ClassificationRace Classification

White/Hispanic/Asian/Black/White/Hispanic/Asian/Black/OtherOther

• Huge Dimensional space, few Huge Dimensional space, few training pointstraining points

• Very few examples of Very few examples of categories other than whitecategories other than white

• Classifiers tend to bias Classifiers tend to bias towards White class towards White class

• Caution: Even a trivial Caution: Even a trivial classifier (classify all as classifier (classify all as

white) can attain a accuracy white) can attain a accuracy of 85%of 85%

What is the solution?What is the solution?

• Gather more training Gather more training data of different data of different

classes.classes.

• Synthesize data from Synthesize data from existing ones.existing ones.

• Boosting Boosting

Race - Race - DiscussionDiscussionLet us try to scrutinize the reasons for fairly poor performance Let us try to scrutinize the reasons for fairly poor performance

ofof

the Parzen Window and K- nearest neighborhood methods.the Parzen Window and K- nearest neighborhood methods.

ReasonReason

1.1. Very few training points are given for the classes other than the Very few training points are given for the classes other than the white class (85%), so by the inherent structure of the dataset, white class (85%), so by the inherent structure of the dataset, the whites points are most likely to be present in any chosen the whites points are most likely to be present in any chosen volume in the space. volume in the space.

2.2. Also it is clear from the projection of data as if white data form a Also it is clear from the projection of data as if white data form a sphere in the space and other classes are wrapped around the sphere in the space and other classes are wrapped around the sphere like ribbons (this is also the case when the data sphere like ribbons (this is also the case when the data inherently don’t have that much dimension in which it is actually inherently don’t have that much dimension in which it is actually projected). Owing to the above mentioned reason whenever we projected). Owing to the above mentioned reason whenever we try to develop a volume around a point, it is more likely to try to develop a volume around a point, it is more likely to contain points of white data than one of itself because of its contain points of white data than one of itself because of its many fold character. many fold character.

3.3. Had it been these faces were projected onto lower dimensional Had it been these faces were projected onto lower dimensional space should have obtained better results for KNN and Parzen. space should have obtained better results for KNN and Parzen. ((IntuitionIntuition))

Fisher Projected Race Fisher Projected Race data on 3Ddata on 3D

Courtesy - Larissa

Results – Results – Age ClassificationAge Classification

KNNKNN Correctly Correctly ClassifiedClassified

AccuraAccuracycy

ChildChild 47/6847/68 69.1169.11%%

TeenTeen 0/830/83 0%0%

AdultAdult 1688/17301688/1730 97.5797.57%%

SenioSeniorr

1/1151/115 0.860.86%%


ParzeParzenn

Correctly Correctly ClassifiedClassified

AccuraAccuracycy

ChildChild 23/6823/68 33.8233.82%%

TeenTeen 0/830/83 0%0%

AdultAdult 1707/17301707/1730 98.6798.67%%

SenioSeniorr

0/1150/115 0%0%


Results – Results – Age ClassificationAge Classification

NNETNNET Correctly Correctly ClassifiedClassified

AccuraAccuracycy

ChildChild 12/6812/68

(3)(3)69.1169.11

%%

TeenTeen 10/8310/83

(3)(3)0%0%

AdultAdult 1390/17301390/1730

(1695)(1695)97.5797.57

%%

SenioSeniorr

19/11519/115

(1)(1)0.860.86

%%


BoostiBoostingng

Correctly Correctly ClassifiedClassified

AccuraAccuracycy

ChildChild 16/6816/68 23.5223.52%%

TeenTeen 27/8327/83 32.5332.53%%

AdultAdult 1219/17301219/1730 70.4670.46%%

SeniorSenior 33/11533/115 28.6928.69%%


Discussion - Discussion - Age ClassificationAge Classification

Q:Q: Although it looks like both child and teens are in almost Although it looks like both child and teens are in almost equal proportion (68 - 83) then why both KNN and Parzen equal proportion (68 - 83) then why both KNN and Parzen always favoring child over teen??always favoring child over teen??

A: A: This proportion is actually reflecting the fact about Testing This proportion is actually reflecting the fact about Testing set, whereas in Training set this thing is much more clearer set, whereas in Training set this thing is much more clearer where number of child (240) dominates number of teen data where number of child (240) dominates number of teen data points significantly. points significantly.

NNETNNET:: Although the evidence curve suggested highest Although the evidence curve suggested highest accuracy can accuracy can be achieved at n = 23 neurons in the hidden be achieved at n = 23 neurons in the hidden layer, I deliberately chosen layer, I deliberately chosen 8 neurons. The idea was to not 8 neurons. The idea was to not allow the net to build complex allow the net to build complex boundaries so that it may boundaries so that it may have good generalization on the unseen data. have good generalization on the unseen data. And the And the results support the argument too. Bracketed results are from results support the argument too. Bracketed results are from

n=23 neurons.n=23 neurons.

Boosting - Boosting - CommentsCommentsIn almost all of the implementations boosting helped In almost all of the implementations boosting helped improving performance of the week classes but have improving performance of the week classes but have not shown very encouraging results. The reasons I not shown very encouraging results. The reasons I sort out may besort out may be::

1.1. The amount of data needed to actually The amount of data needed to actually implement boosting implement boosting was not sufficient.was not sufficient.

2.2. In constructing the second dataset sometimes In constructing the second dataset sometimes we do not we do not find appropriate proportion to maximize find appropriate proportion to maximize disorder at the disorder at the same time utilizing full training same time utilizing full training resource. (A part of the resource. (A part of the expensive data set left expensive data set left unutilized)unutilized)

3.3. More often than not the condition to More often than not the condition to construct third data construct third data set further excludes most of set further excludes most of the remaining data points, the remaining data points, hence reducing the size hence reducing the size of available samples for training.of available samples for training.

Results – Results – Bandana Bandana ClassificationClassification

KNNKNN YESYES NONO AccuraAccuracycy

BANDANABANDANA 11 77 12.512.5%%

NO NO BANDANABANDANA

00 19881988 100%100%


ParzenParzen YESYES NONO AccuraAccuracycy

BANDANABANDANA 88 00 100%100%NO NO

BANDANABANDANA00 19881988 100%100%

Overall Accuracy: Overall Accuracy: 100% 100%

NNETNNET YESYES NONO AccuraAccuracycy

BANDANABANDANA 00 88 0%0%NO NO

BANDANABANDANA00 19881988 100%100%


BOOSTINBOOSTINGG

YESYES NONO AccuraAccuracycy

BANDANABANDANA 33 55 37.537.5%%

NO NO BANDANABANDANA

11 19871987 99.9499.94%%


Results – Results – Moustache Moustache ClassificationClassification

KNNKNN NONO YESYES AccuraAccuracycy

NO NO MOUSTACMOUSTAC

HEHE

15615622

00 100%100%

MOUSTACMOUSTACHEHE

434434 00 0%0%


ParzenParzen NONO YESYES AccuraAccuracycy


HEHE

15615611

11 99.9399.93%%

MOUSTACMOUSTACHEHE

77 427427 98.3898.38%%

Overall Accuracy: Overall Accuracy: 99.59% 99.59%NNETNNET NONO YESYES AccuraAccura

cycy

NO NO MOUSTACHMOUSTACH

EE

382382 5252 88.0188.01%%

MOUSTACHMOUSTACHEE

122122 14401440 92.1892.18%%


BOOSTINBOOSTINGG

NONO YESYES AccuraAccuracycy


HEHE

366366 6868 84.3384.33%%

MOUSTACMOUSTACHEHE

240240 13221322 84.6384.63%%


Results – Results – Glasses ClassificationGlasses Classification


NO NO GLASSESGLASSES

19871987 11 99.9499.94%%

GLASSESGLASSES 88 00 0%0%


ParzenParzen YESYES NONO AccuraAccuracycy


19881988 00 100%100%

GLASSESGLASSES 00 88 100%100%


NNETNNET YESYES NONO AccuraAccuracycy


19601960 2828 98.5998.59%%

GLASSESGLASSES 77 11 14.2814.28%%


BOOSTINBOOSTINGG

YESYES NONO AccuraAccuracycy


19551955 3333 98.3498.34%%

GLASSESGLASSES 55 33 37.5437.54%%


Results – Results – Beard ClassificationBeard Classification


NO BEARDNO BEARD 17011701 1919 98.8998.89%%

BEARDBEARD 275275 11 0.360.36%%


ParzenParzen NONO YESYES AccuraAccuracycy

NO BEARDNO BEARD 17201720 00 100%100%BEARDBEARD 00 276276 100%100%


NNETNNET NONO YESYES AccuraAccuracycy

NO BEARDNO BEARD 16961696 2424 98.6098.60%%

BEARDBEARD 271271 55 1.841.84%%


BOOSTINBOOSTINGG

NONO YESYES AccuraAccuracycy

NO BEARDNO BEARD 16881688 3232 98.1398.13%%

BEARDBEARD 6161 215215 77.8977.89%%


ConclusionConclusion

In this project I have In this project I have made an effort to made an effort to compare various compare various algorithms on various algorithms on various classification tasks such classification tasks such as Gender, Age, Race as Gender, Age, Race and other properties and other properties like Hat, Moustache etc. like Hat, Moustache etc. and where ever possible and where ever possible I also tried to compare I also tried to compare the results obtained the results obtained from simplest ‘Minimum from simplest ‘Minimum distance classifier’distance classifier’

GenderGender PW, GLD, PW, GLD, KNNKNN

RaceRace NNETNNETAgeAge NNET NNET

with with BoostingBoosting

MoustachMoustache, Hat, e, Hat, Beard, Beard, Bandana, Bandana, GlassesGlasses

PWPW

f ace r ecognition e igen-faces with 99 pca coefficients project presentation ashish tiwari...

Documents

matlab data file

bad data issues

testing data points

training pointsvery

training imagesfaces

training faceseigenfaces

bad faces

realworld problem data