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Extracting Discriminative Binary Template for Face Template Protection. Feng Yicheng Supervisor: Prof. Yuen August 31 st , 2009. Content. Introduction Basic Idea Thresholding to Approximation Objective Function Construction Experimental Results Conclusions. Introduction.
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Extracting Discriminative Binary Template for FaceTemplate Protection Feng Yicheng Supervisor: Prof. Yuen August 31st, 2009
Content • Introduction • Basic Idea • Thresholding to Approximation • Objective Function Construction • Experimental Results • Conclusions
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.
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
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).
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
threshold t2 0 x threshold t1 Basic Idea • 2-dimensional scenario for thresholds optimization y Figure 2
MTp Binary template w Original face template p Projection Thresholding 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.
threshold t2 threshold t2 0 0 x x threshold t1 threshold t1 Basic Idea • Projection can make the thresholding fit the data distribution better. Projection Figure 3
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.
Basic Idea • Discriminability measurement (for class Ω): • Within-class variance DW(Ω) • Between-class variance DB(Ω) • Discriminability: DB(Ω)- DW(Ω) • Optimization: w(p): the binary template transfromed from p. wΩ: the reference binary template of class Ω.
Thresholding to Approximation • Normalize p to simplify the thresholding Assume v=(a1, a2 … ak), then the thresholding process turns to is the mean vector of all p.
Thresholding to Approximation • This process is equivalent to: • Substitute v=MTq to this equation, w’(v) turns to subject to Replace the original thresholding
M is orthonormal Objective Function Construction
Objective Function Construction • We can use D’B(Ω) and D’W(Ω) to replace DB(Ω) and DW(Ω). • Denote . subject to qΩrepresents the mean vector of q in class Ω. (distance from e to q in class Ω is small)
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
Experimental Results CMU PIE
Experimental Results FERET
Experimental Results FRGC
Experimental Results • The GARs (FAR=0.01) and Equal Error Rates (EERs).
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.
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.
Q & A Thanks!