fusion by biometrics

Fusion by BiometricsFusion by Biometrics

主講人：李佳明、陳明暘主講人：李佳明、陳明暘指導教授：林維暘指導教授：林維暘

OutlineOutline

IntroductionIntroduction Score normalization methodsScore normalization methods Fusion methodsFusion methods Experiment ResultsExperiment Results ConclusionConclusion

Reference PaperReference Paper

A. K. Jain, K. Nandakumar, and A. Ross, “Score normalization in multimodal biometric systems," Pattern Recognition , 2005.

Why score normalization ?Why score normalization ?

1. The matching scores at the output of the individual matchers may not be homogeneous.

2. The outputs of the individual matchers need not be on the same numerical scale (range).

3. The matching scores at the output of the matchers may follow different statistical distributions.

Why score normalization ?Why score normalization ? Score normalization refers to changing the location

and scale parameters of the matching score distributions at the output of the individual matchers, so that the matching scores of different matchers are transformed into a common domain.

Score normalizationScore normalization

When the parameters used for normalization are determined using a fixed training set, it is referred to as fixed score normalization.

In adaptive score normalization, the normalization parameters are estimated based on the current feature vector.

A good normalization scheme

Robustness refers to insensitivity to the presence of outliers.

Efficiency refers to the proximity of the obtained estimate to the optimal estimate when the distribution of the data is known.

Normalization Techniques

1. Min-max 2. Decimal scaling 3. z-score 4. Median and MAD 5. Double sigmoid function 6. tanh-estimators

1. Min-max normalization Min-max normalization is best suited for the case

where the bounds (maximum and minimum values) of the scores produced by a matcher are known.

We usually shift the minimum and maximum scores to 0 and 1.

xk : the kth matching score before normalization xk’ : the kth matching score after normalization

1. Min-max normalization This method is not robust (i.e., the method is highly

sensitive to outliers in the data used for estimation). Min-max normalization retains the original

distribution of scores except for a scaling factor and transforms all the scores into a common range [0, 1].

2. Decimal scaling For example, if one matcher has scores in

the range [0, 1] and the other has scores in the range [0, 1000], the following normalization could be applied.

The problems with this approach are lack of robustness.

3. z-score The most commonly used score normalization

technique is the z-score that is calculated using the arithmetic mean and standard deviation of the given data.

Both mean and standard deviation are sensitive to outliers and, hence, this method is not robust.

Z-score normalization does not guarantee a common numerical range for the normalized scores of the different matchers.

If the input scores are not Gaussian distributed, z-score normalization does not retain the input distribution at the output.

3. z-score

4. Median and MAD Robust : The median and median absolute

deviation (MAD) are insensitive to outliers and the points in the extreme tails of the distribution.

This normalization technique does not retain the input distribution and does not transform the scores into a common numerical range.

4. Median and MAD

5. Double sigmoid function

The normalized score is given by

where m is the reference operating point and s1 and s2 denote the left and right edges of the region

where the scores in the [0, 300] range are mapped to the [0, 1] range using m = 200, s1 = 20 and s2 = 30.

Generally, m is chosen to be some value falling in the region of overlap between the genuine and impostor score distribution, and s1 and s2 are made equal to the extent of overlap between the two distributions toward the left and right of m, respectively.

6. tanh-estimators The tanh-estimators introduced by Hampel et al. are

robust and highly efficient. The normalization is given by

where μGH and σGH are the mean and standard deviation estimates, respectively, of the genuine score distribution as given by Hampel estimators

Hampel estimatorsare based on the following influence ( )-function:

6. tanh-estimators

Summary of Normalization Techniques

Experimental Results Database of 100 users with three modalities. Each user having five biometric templates for each

modality.

Experimental Results

Feature Level FusionFeature Level Fusion

“Biometric A” feature vectors : X “Biometric B” feature vectors : Y

Normalization -> X’ , Y’ Dimension reduction Combine two vector -> Z’ = { X’ , Y’ }

Reference PaperReference Paper A. Ross and R. Govindarajan. “Feature Level

Fusion Using Hand and Face Biometrics.” In Proc. SPIE Conf. on Biometric Technology for Human Identication II, volume 5779, pages 196-204,Orlando, 2005.

SON, B. and LEE, Y. “The Fusion of Two User-SON, B. and LEE, Y. “The Fusion of Two User-friendly Biometric Modalities: Iris and Face”, friendly Biometric Modalities: Iris and Face”, IEICE Transactions on Information and Systems , IEICE Transactions on Information and Systems , 2006.2006.

Fusion in feature and matching Fusion in feature and matching levellevel

Normalization method : median and MADNormalization method : median and MAD Dimension reduction method : PCA , LDA Matching score fusion method : sum rule

Consider feature vectors {Xi , Yi} and {Xj , Yj} obtained at two different time instances i and j. Fusion in feature level -> { Zi , Zj } Let sX and sY be the normalized match

(Euclidean distance) scores generated by comparing Xi with Xj and Yi with Yj , respectively.

smatch = (sX + sY)/2 be the fused match score obtained using the simple sum rule.

Experimentation A set of 500 face images and hand images were

acquired from 100 users (5 biometric samples per user per biometric)

Each face image was decomposed into its component R, G, B channels. Further, the grayscale rendition of the color image - I - was also computed.

The Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed on these component images (i.e., R, G, B, I) in order to extract representational features.

The Fusion of Two User-friendly The Fusion of Two User-friendly Biometric Modalities: Iris and FaceBiometric Modalities: Iris and Face

Fusion methodFusion method Dimension reduction method :

Wavelet Transform LDA

Experimentation

Face Databases: IISFace : We sampled frontal face images of 100

subjects from the IIS face database. Each subject has 10 images with varying expressions.

Iris Databases: Iris1 : This data set consists of 1000 iris images

acquired from 100 individuals. (good quality images)

Iris2 : The Iris2 database consists of 1000 iris images containing some bad quality ones acquired from 100 individuals.

Experimentation Face Databases:

ORLFace : The ORL data set consists of 400 frontal faces: 10 tightly cropped images of 40 subjects with variations in poses, illuminations, facial expressions and accessories.

Iris Databases: Iris3 : The Iris3 database is composed of 400

good quality images sampled from the Iris1 database to combine with the ORLFace database.

Iris4 : The Iris4 is composed of 400 iris images containing some bad quality ones sampled from the Iris2 database to combine with the ORLFace.

M. Indovina, U. Uludag, R. Snelick, A. M. Indovina, U. Uludag, R. Snelick, A. Mink and A. Jain, "Multimodal Biometric Mink and A. Jain, "Multimodal Biometric Authentication Methods: A COTS Authentication Methods: A COTS Approach", Approach", Proc. MMUA 2003, Workshop Proc. MMUA 2003, Workshop on Multimodal User Authenticationon Multimodal User Authentication, pp. , pp. 99-106, Santa Barbara, CA, December 99-106, Santa Barbara, CA, December 11-12, 2003. 11-12, 2003.

abstract

We examine the performance of multimodal biometric authentication systems using Commercial Off-the-Shelf (COTS) fingerprint and face biometrics.

It introduce novel methods of fusion and normalization that improve accuracy still further through population analysis.

Normalization methods

a matcher score as s from the set S of all scores for that matcher and the corresponding normalized score as n.

Min-MaxMin-Max maps the scores to the [0, 1] range. maps the scores to the [0, 1] range.

Z-scoreZ-score transforms the scores to a distribution with transforms the scores to a distribution with

mean of 0 and standard deviation of 1. mean of 0 and standard deviation of 1.

TanhTanh robust statistical techniques. maps the scores to the (0, 1) range.

AdaptiveAdaptive Using an adaptive normalization procedure

that aims to increase the separation of the genuine and impostor distributions.

Two-Quadrics composed of 2 quadratic segments that

change concavity at c.

Normalization methods LogisticLogistic

logistic function The general shape of the curve is similar to Two-

Quadrics

f(0) is equal to the constant Δ, which is selected to be a small value (0.01 in this study).

Quadric-Line-Quadric The overlapped zone, w, is left unchanged

while the other regions are mapped with two quadratic function segments.

Fusion methodsFusion methods

Simple Sum Scores for an individual are summed.

Min Score Choose the minimum of an individual’s

scores.

Max Score Choose the maximum of an individual’s

scores.

Matcher Weighting Matcher weighting-based fusion makes use of

the Equal Error Rate (EER).

the weights are inversely proportional to the corresponding errors.

The fused score is calculated as

EER if the score distributions overlap, the FAR and

FRR intersect at a certain point. The value of the FAR and the FRR at this point, which is of course the same for both of them, is called the Equal Error Rate (EER).

Fusion methodsFusion methods User Weighting

This method applies weights to individual matchers differently for every user (individual).

The calculation of these user-dependent weights make use of the wolf-lamb concept introduced by Doddington, et al.

We assume that for every (i, m) pair, the mean and standard deviation of the associated genuine and impostor distributions are known.

use the d-prime metric as a measure of the separation of these two distributions.

Experiment ResultsExperiment Results

The best EER values in individual columns are indicated with bold typeface; the best EER values in individual rows are indicated with a star (*) symbol.

Normalization: varied, fusion: Simple Normalization: varied, fusion: Simple Sum Sum

Normalization: varied, fusion: Min ScoreNormalization: varied, fusion: Min Score

Normalization: varied, fusion: Max ScoreNormalization: varied, fusion: Max Score

Normalization: varied, fusion: Matcher Normalization: varied, fusion: Matcher weightweight

Normalization: varied, fusion: User Normalization: varied, fusion: User weightweight

Normalization: Min-Max, fusion: variedNormalization: Min-Max, fusion: varied

Normalization: Z-score, fusion: variedNormalization: Z-score, fusion: varied

Normalization: Tanh, fusion: variedNormalization: Tanh, fusion: varied

Normalization: Normalization: Quadric-Line-Quadric, , fusion: variedfusion: varied

Normalization: Normalization: Quadric-Line-Quadric, , fusion: Simple Sumfusion: Simple Sum

Y. Wang, T. Tan and A. K. Jain, Y. Wang, T. Tan and A. K. Jain, "Combining Face and Iris Biometrics for "Combining Face and Iris Biometrics for Identity Verification", Identity Verification", Proc. of 4th Int'l Proc. of 4th Int'l Conf. on Audio- and Video-Based Conf. on Audio- and Video-Based Biometric Person Authentication Biometric Person Authentication (AVBPA)(AVBPA), pp. 805-813, Guildford, UK, , pp. 805-813, Guildford, UK, June 9-11, 2003. June 9-11, 2003.

abstractabstract

Combining Face and Iris to improve Combining Face and Iris to improve verification performance.verification performance.

Fusing in scores level.Fusing in scores level. We use two different strategies.

The first strategy is to compute either an unweighted or weighted sum .

The second strategy is to treat the matching distances of face and iris classifiers as a two-dimensional feature vector and to use a classifier such as Fisher’s discriminant analysis.

Weighted Sum RuleWeighted Sum Rule

Fisher Discriminant AnalysisFisher Discriminant Analysis we treat the face and iris matcher outputs x1 we treat the face and iris matcher outputs x1

and x2 as a feature vector X = (x1 ,x2).and x2 as a feature vector X = (x1 ,x2).

Decision RuleDecision Rule

Distribution of matching distancesDistribution of matching distances

Total error rateTotal error rate

Michalis Petrakos, Jon Atli Benediktsson, and Ioannis Kanellopoulos,” The Effect of Classifier Agreement on the Accuracy of the Combined Classifier in Decision Level Fusion”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 11, NOVEMBER 2001

abstractabstract Agreement among classifiers can inhibit the

gains obtained regardless of the method used to combine them. In this work, the level of agreement between different classifiers used in remote sensing is assessed based on statistical measures.

A study is performed in which an image is classified by several methods with different degrees of agreement between them.

As stated previously, the LOP and the LOGP are widely used decision fusion approaches.

LOP (Linear Opinion Pool)LOP (Linear Opinion Pool)

LOGP (Logarithmic Opinion Pool)LOGP (Logarithmic Opinion Pool)

Then we can combine LOP/LOGP and Then we can combine LOP/LOGP and neural network.neural network.

Their combination schemes were considered to be two stage processes: statistical classifiers in stage one, and a single neural network in stage two.

Their suggested voting schemes were: Majority Voting: When the majority of the

individual agree on the classification of a sample, the sample is classified to that class.

Complete Agreement: When all the individual source-specific classifiers agree on the classification of a sample, the sample is classified to that class.

CONSNN-NN: A neural network and a LOGP classifier are trained separately on all the data.

DatabaseDatabase

Experiment ResultsExperiment Results The minimum distance (MD) computes the

Mahalanobis distance with every one of the 12 land-use classes for each sample, and assigns the sample to the class with the smallest distance.

Linear discriminant analysis (LDA) assigns a sample to the class with the maximum posterior probability given that all classes have a common (pooled) covariance matrix.

Quadratic discriminant analysis (QDA) assigns a sample to the class with the maximum posterior probability. In this case, each class has its own covariance matrix.

A conjugate gradient neural network (CGNN) with 13 inputs, one hidden layer containing 20 neurons, and 12 outputs.

ConclusionConclusion

Some fusion methods are the best in a biometric system, but they maybe not the best in other biometric system.

The effects of different score normalization techniques in a multimodal biometric system have been studied.

fusion by biometrics

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