1 extracting discriminative binary template for face template protection feng yicheng supervisor:...
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1
Extracting Discriminative Binary Template for FaceTemplate Protection
Feng Yicheng
Supervisor: Prof. Yuen
August 31st, 2009
2
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.
4
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
5
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).
6
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
7
Basic Idea
2-dimensional scenario for thresholds optimizationy
x0
threshold t2
threshold t1
Figure 2
8
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
9
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
10
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.
11
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
12
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
13
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
14
Objective Function Construction
pp
W wpwD 1))(()(2
ppB wpwD 1))(()(
2
Wk
MwqD 1)()('
2
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 )()('
15
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)
16
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
17
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
18
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
19
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
20
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
22
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.