1

Extracting Discriminative Binary Template for FaceTemplate Protection

Feng Yicheng

Supervisor: Prof. Yuen

August 31st, 2009

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Content

1. Introduction

2. Basic Idea

3. Thresholding to Approximation

4. Objective Function Construction

5. Experimental Results

6. Conclusions

3

Introduction

Biometric for personal authentication has been used in many applications.

Since biometric is the “unique” feature, it is hard to reset or re-issue.

Security and privacy concern Non-invertible: The attacker can’t extract the original templates

with the data stored in database.

Cancelable: If some templates are compromised, new templates can be generated to replace them.

Application-specific: Different applications should use different versions of templates.

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Introduction

Biometric cryptosystem approach is applied for protection Require binary input

Existing approaches apply thresholding to binarize the original biometric templates Discriminability may be affected with the binarization Effect to discriminability has not been evaluated

Objective: Find an approach to discriminatively binarize the face

templates

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Basic Idea

Use thresholding for binarization Directly optimizing thresholds has some

problems Contradict to the max-entropy rule

Max-entropy rule: to gain maximum information content, the thresholds should be set to make half of the transformed bits to be 1, half to be 0.

Not effective Thresholds satisfying the max-entropy rule provides

highest information content, already implying certain discriminability (Figure 1).

Optimizing thresholds may not fit the data distribution (Figure 2).

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Basic Idea

“Mean”: the thresholds are set as the mean values of the original templates.

“Random”: thresholds are randomly chosen with a Gaussian distribution Mean of the distribution is mean

of the original templates Variance of the distribution is r

times of the variance of the original templates.

Tested 100 times, choose the average.

Figure 1

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1ROC

False accept rate

Gen

uine

acc

ept

rate

Mean

Random r=2

Random r=10Random r=20

Random r=50

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Basic Idea

2-dimensional scenario for thresholds optimizationy

x0

threshold t2

threshold t1

Figure 2

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Basic Idea

To fit the data distribution better, choose a projection before threhsolding First do an projection, then do thresholding.

Fit data distribution better (Figure 3) The projection should not degrade the

discriminability: choose orthonormal matrix. The projection is discriminability preserving.

ProjectionOriginal facetemplate p

MTpThresholding

Binary template w

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Basic Idea

Projection can make the thresholding fit the data distribution better.

x0

threshold t2

threshold t1

x0

threshold t2

threshold t1

Projection

Figure 3

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Basic Idea

Proposed scheme Original template p is first projected with orthonormal

matrix M: u=MTp

u=(g1, g2 … gk) is then thresholded to binary template

(b1, b2 … bk) with thresholds t1, t2 … tk. Due to the max-entropy rule, ti should be the mean value of gi.

Find optimal M to maximize the discriminability of the extracted binary templates.

For different classes, we choose different M.

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Basic Idea

Discriminability measurement (for class Ω): Within-class variance DW(Ω)

Between-class variance DB(Ω)

Discriminability: DB(Ω)- DW(Ω)

Optimization:

pp

W wpwD 1))(()(2

pp

B wpwD 1))(()(2

w(p): the binary template transfromedfrom p.

wΩ: the reference binary template of class Ω.

)()(minarg),(,

WBwM

optopt DDwM

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Thresholding to Approximation

Normalize p to simplify the thresholding

Assume v=(a1, a2 … ak), then the thresholding process turns to

0 if 1

0 if 1

i

ii a

ab

pp

ppq

qMv T

is the mean vector of all p. p

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Thresholding to Approximation

This process is equivalent to:

Substitute v=MTq to this equation, w’(v) turns to

kvvw

minarg)(' subject to k1,1

qk

qMw

k

Mq

kqMqw T

)(''minarg

minarg)(''

Replace the original thresholding

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Objective Function Construction

pp

W wpwD 1))(()(2

ppB wpwD 1))(()(

2

qq

Wk

MwqD 1)()('

2

qq

Bk

MwqD 1)()('

2

pp

wqw 1))(''(2

pp

wqw 1))(''(2

pp

MwqMw 1))(''(2

pp

MwqMw 1))(''(2

)('')( qwpw

M is orthonormal

qk

qMw

)(''

kDD WW )()(' kDD BB )()('

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Objective Function Construction

We can use D’B(Ω) and D’W(Ω) to replace DB(Ω) and DW(Ω).

Denote .k

wMe

q

q

q

q

eopt

qeqe

e11

minarg

22

qeT

subject to

qΩ represents the mean vector of q in class Ω.

(distance from e to q in class Ω is small)

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Experimental Results

Experiment settings Three common face databases used

CMU PIE (68x105x10) FERET (250x4x2) FRGC (350x40x5)

Fisherface algorithm applied for feature extraction

Compared with the RMQ algorithm

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Experimental Results

0 0.1 0.2 0.3 0.40.5

0.6

0.7

0.8

0.9

1ROC

False accept rate

Gen

uine

acc

ept

rate

Original

RMQ

TOP

CMU PIE

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Experimental Results

0 0.1 0.2 0.3 0.40.4

0.5

0.6

0.7

0.8

0.9

1ROC

False accept rate

Gen

uine

acc

ept

rate

Original

RMQ

TOP

FERET

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Experimental Results

0 0.1 0.2 0.3 0.40.4

0.5

0.6

0.7

0.8

0.9

1ROC

False accept rate

Gen

uine

acc

ept

rate

Original

RMQ

TOP

FRGC

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Experimental Results

The GARs (FAR=0.01) and Equal Error Rates (EERs).

GAR Original RMQ TOP

CMU PIE 59.26 73.53 85.99

FERET 45.47 74.01 85.09

FRGC 26.28 67.40 78.35

EER Original RMQ TOP

CMU PIE 17.32 10.37 6.30

FERET 21.66 11.29 7.44

FRGC 31.75 13.38 10.05

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Security Analysis

The reference binary templates are randomly generated, provide k bits entropy.

Projection matrix M is unprotected. However, since M is only related to wΩ with equation and e is kept secret to attacker, M will not release useful information.

kwMe

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Conclusions

This paper has proposed a new method to generate a binary face template from a real valued face template.

The discriminability of the extracted binary templates is optimized.

The experimental results show that the proposed method has good performance.

The security of the proposed algorithm is just the length k of the extracted binary template, which is quite sufficient when k is large.

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Q & A

Thanks!

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Appendix

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