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MODULAR IMAGE PRINCIPAL

COMPONENT

ANALYSIS

(MIMPCA)

Jos Francisco, George Darmiton da Cunha, Tsang Ing Ren

{jfp, gdcc, tir}@cin.ufpe.br

1Jos Francisco {[email protected]}


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Access control is a subject of major interest

nowadays

Biometry is widely used for access control

Face recognition stands out as a non-intrusive

biometric measure

PCA-based approach represents the state of the art

in face recognition Changes in illumination, head pose and facial

expression affects considerably the recognition rates

Introduction

2 Jos Francisco {[email protected]}


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Reduce computational cost of PCA-base techniques

by reducing its complexity

Minimize the environment effects over final

classification rate

Improve face recognition accuracy

Extract more accurate features by combining

different PCA approaches

Objective



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Face Recognition Systems can be divided in five

major steps

Face Recognition System


Data acquisition

Face detection

Feature extraction

Face recognition

Identification or verification


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Data acquisition

Uses different technology for capture different data types

Face detection

Face information are extracted from original data

Feature extraction

Transforms the original data in new ones (more

representative)

Face recognition

Identifies the images based on training data




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This work focus on feature extraction step

Its one of the most important steps for recognition

rate improvement

Changes in illumination, head pose and facial

expression affects drastically the feature extracted

Holistic approaches are substantially affected by this

changes

Poor extraction implies low classification accuracy

Local information can increase feature quality

extraction




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Introduced by Pentland [1] in 1991

PCA based techniques involves pattern statistical

analysis

Highly used for feature extraction and face

recognition

Number of samples and dimensionality affects its

representation power Sample Size Problem

PCA for Feature Extraction



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The step which images data are transformed in new

ones simpler and generally smaller

PCA based techniques have at least two problems at

this step

Environment changes drastically affects the data

representation

Extracts poor statistical data due to its relatively small

number of sampler compared to its dimensionality

Feature Extraction



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Reduce environment changes effects

Use modular approaches instead of holistic

Contextualized vs. General partitions

Improve statistical representation quality

Use a more compact representation

Increase the number of instances (artificially)

Feature Extraction



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Modular Principal Component Analysis (MPCA)

Splits images in small regions without previous image

analysis

Applies PCA on each sub pattern

Reach better results than traditional PCA by using local

regions without changes

Expected better results for images with partial variations in

illumination, facial expression and head pose.

Feature Extraction



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Modular Principal Component Analysis

Each image is divided in Nparts.

Just one covariance matrix is used

For each region is applied PCA and extracted their weights

Each region has dprincipal components extracted resulting

on dxNcomponents per image

Technique is very similar to the eigenfaces

Feature Extraction



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Two-Dimensional Principal Component Analysis

(IMPCA)

Represents the images as matrixes

Avoid the 1D transformation improving computational cost

Relatively small covariance matrix

Extracted features is more representative. Reducing the

sample size problem.

Minor cost for extract these weights

Uses more data to represent de images

Feature Extraction



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Uses general partition for split the original images in

Nnew ones

Each sub-image is represented as a matrix

Just one mean and one covariance matrix are

calculated based on all resulting images To link all images with each others

Modular Image Principal Component

Analysis

13


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A projection matrix is defined based on eigenvectors

with largest eigenvalues

Each principal component is a vector instead of

traditional scalar value

So, final face projection results on a image matrix Smaller than original and more representative


Analysis



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The test images as projects over the P matrix

Their distances are calculated based on each feature

vector distance (columns of matrix) followed by the

distance of previous generated vectors


Analysis



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Experiments


The experiments were performed over databases

that explores head pose, illumination changes, facial

expression and use of object over face

The 3-knn was adopted for each image regionclassification

The final class is calculated based on individual

classification of the regions

Experiments were performed using five images for

training and five for testing


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Experiments


The best number of face regions was defined

experimentally

For ORL database:


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Experiments


Results using Yale


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Experiments


In general, better results were reached on low

dimensionality

The technique quickly stabilizes

The best results were reached using the MIMPCA

technique

In high dimensionality the MPCA and MIMPCA are

very close The modular approaches use nine images per face


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Experiments


Results over ORL


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Experiments


Again MIMPCA reach the best results in low

dimensionality

In high dimensionality it has a worse accuracy rate

This experiments were performed using four regions

per image


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Experiments


Experiments using UMIST


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Experiments


UMIST face database has relatively low results due to

the large difference in head pose of database images

Better results were reached on low dimensionality

and quickly stabilizes

Again, the images were divided in four regions


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Final Considerations


MIMPCA out performs the others techniques on low

dimensionality, decreasing these difference on high

dimensionality

Its quickly stabilizes

It reaches the best accuracy rate in all experiments as

summarized bellow

MPCA IMPCA MIMPCAUMIST 63,00 62,00 65,00

ORL 94,44 93,00 95,00

Yale 94,44 91,11 96,67


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Final Considerations


MIMPCA also reaches the best mean computational

cost

However, MIMPCA uses more coefficients per

components increasing its storage requirements

MPCA IMPCA MIMPCA

Time (s) 443 34 19


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FUTURE (AND PRESENT) WORKS



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Future Works

Build a final principal component matrix combining

the result matrix of each regions

Build a system of weights for combining matrixes

Use a search technique to find the best weight set

Genetic Algorithm



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Future Works

Use different combinations of individual results

Use the final result of each region for finding the final

result class of the face

Use all [three] intermediate results of each face region to

define the final classification


R1 = [C1 C1 C4] = C1R2 = [C2 C2 C2] = C2R3 = [C2 C1 C1] = C1R4 = [C2 C3 C3] = C3

R1 = [C1 C1 C4]

R2 = [C2 C2 C2]

R3 = [C2 C1 C1]

R4 = [C2 C3 C3]

Rf= [C1 C2 C1 C3] = C1

Rf= [C1 C1 C4 C2 C2 C2 C2 C1 C1 C2C3 C3] = C2


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Future Works

Current results


Best Individual (1) All SubImg Results (2) wMIMPCA (3)

Mean Std Mean Std Mean Std1 (2 x 2) 77,75 4,48 90,25 2,75 97,25 2,79

2 (2 x 2) 80,25 5,83 90,50 4,05 97,00 3,85

3 (2 x 2) 82,75 5,06 92,00 3,69 98,25 2,31

4 (2 x 2) 83,00 3,29 91,50 3,37 98,50 2,00

5 (2 x 2) 90,50 4,38 95,75 3,34 98,75 1,72

1 (3 x 2) 88,75 3,95 94,50 3,69 98,50 1,55

2 (3 x 2) 89,00 4,44 95,50 4,53 98,75 2,13

3 (3 x 2) 90,00 4,56 94,25 2,90 98,75 2,18

4 (3 x 2) 90,50 3,69 96,25 2,12 98,50 2,50

5 (3 x 2) 88,50 4,59 94,25 2,37 99,25 2,48

1 (2 x 1) 91,00 5,43 97,00 2,30 98,75 2,16

2 (2 x 1) 92,50 2,36 94,75 3,43 98,75 1,95

3 (2 x 1) 89,25 7,17 95,00 2,04 98,75 2,00

4 (2 x 1) 88,25 4,57 95,75 2,90 98,50 2,30

5 (2 x 1) 93,25 2,90 95,25 3,43 98,75 2,13

1 (1 x 2) 91,50 4,28 98,25 1,69 99,25 1,61

2 (1 x 2) 88,25 6,02 96,25 2,12 98,00 3,40

3 (1 x 2) 88,25 5,14 95,75 2,90 98,75 2,09

4 (1 x 2) 92,00 3,50 95,75 4,26 98,00 2,48

5 (1 x 2) 92,25 3,43 96,00 3,16 98,75 2,18Mean 88,38 4,45 94,73 3,05 98,49 2,29


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References

M. Turk, A. Pentland. Eigenfacesfor Recognition. Journal of Cognitive

Neuroscience. Vol. 3, No. 1. 71-86, 1991.

Yang, J., Zhang, D., Frangi, A.F. e Yang, J. (2004) Two-dimensional PCA: A

newapproach to appearance-Based Face Representation and Recognition,

IEEE transactions on pattern analysis and machine intelligence, Vol. 16,No. 1, pp. 131 137

GOTTMUKKAL, R. e ASARI, V. K.An improved face recognition technique

based on modular PCA approach. Pattern Recognition Letters 15 (2004)

pp. 429-436.

PEREIRA, Jos Francisco ; CAVALCANTI, George Darmiton da Cunha; TSANGIng Ren .Modular Image Principal ComponentAnalysisfor Face

Recognition (accepted). In: IJCNN, 2009, Atlanta. 2009

ROCHA, L.M., Singular Value Decomposition and principal component

analysisin A Pratical Approach inMicroarray Data Analysis. Portland State

University Ph.D. Program, Kluwer: Norwel, pp. 91-109.31 Jos Francisco {[email protected]}


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MODULAR IMAGE PRINCIPAL

COMPONENT ANALYSIS (MIMPCA)

Jos Francisco, George Darmiton da Cunha, Tsang Ing Ren

{jfp, gdcc, tir}@cin.ufpe.br


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