face recognition using fuzzy fisherface classifiercsajaykr/myhome/teaching/biometrics/face_2.pdf ·...

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Face Recognition Using Fuzzy Fisherface Classifier

Presenters:Nilesh PadwalVivek K.Rajat Rastogi

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Contents

Fuzzy Fisherface AppproachAlgorithmFlowchartYale DatabaseORL DatabaseComparison of Recognition RatesConclusionReferences

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Fuzzy Fisherface Approach

More sophisticated usage of class assignment of patterns (faces)

Classification results affect the within-class and between-class scatter matrices

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The Computations Of Membership Degrees

Compute the membership grade to class for pattern ,

0.51 0.49( / ) if same as the label of the pattern

0.49( / ) if same as the label of the patternij

ij

n k i jth

n k i jth

+ =⎧⎪⎨ ≠⎪⎩

i jth

where is number of the neighbors of theijnjth data that belong to ith class

ijµ

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AlgorithmResults of FKNN are used in computations of mean value and scatter covariance matrices,Mean vector of each class

The between class and within class fuzzy scatter matrices are respectively,

~1

1

N

i j jj

i N

i jj

Xm

µ

µ

=

=

=∑

∑

~ ~

1

~ ~

1 1

( )( )

( )( )i

k i

cT

i iFB ii

c cT

i iFW k k FWi x C i

S N m m m m

S x m x m S

=

= ∈ =

= − −

= − − =

∑

∑ ∑ ∑

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AlgorithmThe optimal fuzzy projection WF-FLD and feature vector transformed by fuzzy fisherfacemethod are given by

~

arg max

( )

TFB

F FLD TWFB

T T Ti F FLD i F FLD i

W S WW

W S W

v W X W E z z

−

− −

=

= = −

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Flowchart

courtesy:Source [1]

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Yale DatabaseTotal Images- 165, total Classes- 15 (11 Images For Each Class) One Image for each configuration: Center-light, glasses/no glasses, happy, normal, left/right light, sad, sleepy, surprised, wink.

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Yale Database

Mugshots were acquired using digicam,Each image was digitized and presented by a 243 X 320 pixel array

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ORL DatabaseTotal Images: 400, total classes: 40 (10 Images for each class)Mugshots were acquired using DigiCam, varying facial detailsEach image was digitized and presented by a 112 X 92 pixel array

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Comparison of Mean Recognition Rates (Yale)

93.8791.9470.85Case 3

(5-training,6-testing)

96.2496.0471.66Case 2

(7-training,4-testing)

94.293.472.44Case 1

(6-training,5-testing)

Fuzzy Fisherface(Fuzzy+PCA+LDA) (%)

Fisherface(PCA+LDA)(%)

Eigenface (PCA)(%)

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Comparison of Mean For Recognition Rates (ORL)

93.5093.3886.94Case 3

(4-training,6-testing)

95.594.7591.13Case 2

(5-training,5-testing)

97.1295.5990.94Case 1

(6-training,4-testing)

Fuzzy Fisherface(Fuzzy+PCA+LDA)

(%)

Fisherface(PCA+LDA)(%)

Eigenface (PCA)(%)

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Comparison of Recognition Rate For Yale Database

0

20

40

60

80

100

Case 1 Case 2 Case 3

EigenfaceFisherfaceFuzzy-Fisherface

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Comparison of Recognition Rate For ORL Database

80828486889092949698

Case 1 Case 2 Case 3

EigenfaceFisherfaceFuzzy-Fisherface

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Confusion Matrix (Yale)Case 2(7/4)

Fisherface Fuzzy Fisherface

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Comparison

Fisherface

Fuzzy Fisherface

Eigenface

Input Image Matched Image

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Comparison

Fisherface

Fuzzy Fisherface

Eigenface

Input Image Matched Image

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Comparison

Fisherface

Fuzzy Fisherface

Eigenface

Input Image Matched Image

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ConclusionFuzzy fisherface approach outperforms the other two methods for the datasets considered.

Sensitive to variations in illumination and facial expression reduced substantially.

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ReferencesKeun-Chang Kwak, Witold Pedrycz : Face Recognition Using Fuzzy Fisherface Classifier, Journal of Pattern Recognition 38(2005),1717-1732Turk, M., Pentland, A.: Eignefaces for Recognition. Journal of Cognitive Neuroscience, Vol.3, (1991) 72-86Turk, M., Pentland, A.: Face Recognition Using Eignefaces. In Proc. IEEE Conf. On Computer Vision and Pattern Recognition. (1991) 586-591Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfacesvs. Fisherfaces: Face Recognition using class specific linear projection. In Proc. ECCV, (1996) 45-58Yale Face Database, http://cvc.yale.edu/projects/yalefaces/yalefaces.htmlORL Face Database, http://www.uk.research.att.com/facedatabase.html

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Thank You