biometric authentication systems

Biometric Authentication Systems

林維暘中正大學資訊工程學系九十五學年度第二學期

Biometric Authentication 2

Agenda

§ 2.1 Introduction2.1 Introduction

§ 2.2 Design Tradeoffs

§ 2.3 Feature Extraction

§ 2.4 Adaptive Classifiers


Introduction

• There is a rapidly increasing interest in the development of commercial systems for biometric authentication applications.

• The objective of a commercial system is to satisfy security requirement while incurring minimal cost.

• This chapter discusses system deployment requirements as well as critical design tradeoffs.


Agenda

§ 2.1 Introduction

§ 2.2 Design Tradeoffs2.2 Design Tradeoffs




2.2 Design Tradeoffs

§ 2.2.1 Accuracy vs. Intrusiveness

§ 2.2.2 Recognition vs. Verification

§ 2.2.3 Centralized vs. Distributed

§ 2.2.4 Processing Speed

§ 2.2.5 Storage Requirements

§ 2.2.6 Compatibility between Feature Extractor and Classifier


2.2 Design Tradeoffs

• To evaluate a biometric system’s accuracy, the most adopted metrics are– False Rejection Rate (FRR)– False Acceptance Rate (FAR).


False Rejection Rate

• FRRFRR, or miss probability, is the percentage of authorized individuals rejected by the system.

• Sensitivity, a.k.a. True Positive Rate (TPRTPR), is the percentage that an authorized person is admitted.

FRR = 1 - TPR


False Acceptance Rate

• FARFAR, a.k.a. False Positive Rate (FPRFPR), is the percentage that unauthorized individuals are accepted by the system.

• Specificity, a.k.a. True Negative Rate (TNRTNR), is the percentage that an unauthorized person is correctly rejected.

FAR = FPR = 1 - TNR


The ROC Curve

• A good authentication system should have both low FRR and low FAR.

• Typically, the tradeoff is illustrated by so-called Receiver Operation Characteristic (ROCROC) curves or by the Detection Error Tradeoff (DETDET) curves.

• Tradeoff between FAR and FRR is adjusted by varying the threshold.


The ROC Curve


ROC and DET curves

10-4

10-3

10-2

10-1

100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Acceptance Rate (FAR)

Tru

e P

ositi

ve R

ate

(TP

R)

ROC curve

10-4

10-3

10-2

10-1

100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Acceptance Rate (FAR)

Fal

se R

ejec

tion

Rat

e (F

RR

)

DET curve


2.2.1 Accuracy vs. Intrusiveness

• Physiological characteristics (e.g., fingerprint and iris) generally provide higher accuracy than behavioral features (e.g., voice and signature).– Behavioral features can change from daty to d

ay.– Physiological characteristics always remain th

e same.



• If a security system makes users feel uncomfortable, then it is intrusive.

• For low security level environments (e.g. apartments, hotels), an intrusive system is highly undesirable.

• On the other hand, intrusive systems are commonly deployed in high security areas.



Intrusiveness Convenience Error rate

Face No Good 10-1 ~ 10-3

Palm No? Middle < 10-3

Fingerprint Yes Middle 10-2 ~ 10-6

Iris Yes Bad < 10-6

Voice No Middle 10-1 ~ 10-2

Signature No Bad 10-1 ~ 10-3


2.2.2 Identification vs. Verification

• Identification– Search a database for an acceptable match– Higher computational cost– Higher error rate

• Verification– Verify the identity of a user– Greatly reduced FAR– Slightly increased FRR


2.2.3 Centralized vs. Distributed

• Three major components in a biometric system– Sensor– Pattern matcher– Controller

• These pieces can be configured in various ways.– Centralized– Distributed

Centralized system architecture 18

Central matcher

& controller Central

template database

Central transaction logging

sensor

sensor sensor

sensor

user

Distributed system architecture 19

Central controller

Central template database

Central transaction logging

sensorsensor

sensorsensor

Local DB Local DB

Local DB Local DB

matcher matcher

matcher matcher

user


2.2.3 Centralized vs. Distributed

Distributed System Centralized System

Less communication loading More communication loading

Lower risk of system failure Higher risk of system failure

Maintenance is more complex

Less management issues


2.2.4 Processing Speed

• If a gateway control system takes on hour to process one entry request, it is useless no mater how accurate it is.

• Fingerprint identification system– 18 types of fingerprint features– Error rate of 10-10 can be achieved– Accuracy is usually sacrificed for speed

[198,295]


2.2.5 Data Storage Requirements

• In most scenarios, the size of raw data is too large to store.

• Raw data is compressed into feature vectors with much smaller dimension.– Pentland et al. [272] compress a 256 x 256 im

age to a 20-dimnesional feature vector.

• Application types dictate the system architecture– e.g., Central or local database


2.2.6 Compatibility between Feature Extractor and Classifier

• A recognition system involves mapping between the following spaces. – Instantiation space: A symbol is instantiated into an

object. A symbol may have different instantiations.– Feature space: The mapping from instantiation space

to feature space is called feature extractionfeature extraction.– Symbol space: The symbols represent classes of

objects. The mapping from feature space to symbol space is called classificationclassification.


Compatibility between Feature Extractor and Classifier

• Feature extraction– The most important stage in a recognition syst

em– Represented by a mapping from instantiation

space x to feature space v.

x → v = f(x)



• Classification– The mapping from feature space to symbol sp

ace– A two-class classifier

• Discriminant function (v)• (v) > 0 if feature vector is extracted from an insta

ntiation belonging to one class.• (v) < 0 if feature vector is extracted from an insta

ntiation belonging to the other class.



• In order to design an effective system, one needs to consider not only feature extraction but also classification.

Feature Extractor

Pattern Classifier

(e.g. neural networks)Raw Data

(e.g. speech waveform, fingerprint images, facial images)

Feature Vectors

Classification Decisions

(e.g. ID of claimants, accept/reject)


Agenda

§ 2.1 Introduction


§ 2.3 Feature Extraction2.3 Feature Extraction



2.3 Feature Extraction

§ 2.3.1 Criteria of feature extraction

§ 2.3.2 Projection methods for dimension reduction

§ 2.3.3 Feature selection

§ 2.3.4 Clustering methods


2.3.1 Criteria of Feature Extraction

• Data compression– Only vital representations are extracted.

• Informative ness– The characteristics essential for the intended

applications should be best described.

• Invariance– The dependency on environmental conditions should

be minimized.

• Ease of processing– A cost-effective implementation should be feasible.


2.3.1 Criteria of Feature Extraction

• Two approaches are often adopted to obtain compressed representation.– Dimension reduction by projection onto linear

subspace– Data clustering (Chapter 3)


2.3.2 Projection Methods for Dimension Reduction

• Principal Component Analysis (PCAPCA)– A mapping from Rn to Rm, n > m– Mathematically, the PCA is to find a matrix W

such that

y = W x, where W is an mxn matrix

– The W is formed by the m eigenvectors corresponding to the largest m eigenvalues


2.3.2 Projection Methods for Dimension Reduction

• Independent Component Analysis (ICAICA)– ICA extracts components with higher-order

statistical independence.– Kurtosis of a random variable is defined as

22

4

][

][)(

YE

YEYk


Independent Component Analysis

1. Gaussian:

2. Uniform:

3. Binary:

k(y) 3

k(y) 1.8

k(y) 1



• PCA maximizes the second-order covariance.

• ICP maximizes the fourth-order kurtosis.– An advantage of using ICA is that kurtosis

function is scale invariant.– The most discriminative independent

component

22

4

])[(

])[(min

wx

wxw E

E



• Mathematically, the ICA is to find a matrix W such that

y = W x, where W is an mxn matrix

– y contains the m most discriminative independent components.

– The W is formed by the m independent row vectors wi, which can be extracted sequentially.


2.3.3 Feature Selection

• Sometimes, only a few selected features would suffice.– In Hong Kong stock market, only 33 stocks ar

e selected to calculate the Hang Seng index.

• Note that unlike dimension reduction, there is no linear combination in the feature selection.



• Fisher Discriminant Analysis– Fisher discriminant J(xi) represents the ration of inter-

class distance to intra-class variance

– 1i and 2i denote the means of xi belonging to class 1 and class 2, respectively.

– 1i and 2i denote the variances of xi belonging to class 1 and class 2, respectively.

22

21

221 )(

)(ii

iiixJ



• Fisher Discriminant Analysis– The value of J(xi) provides a simple mean for f

eature selection.– The selected features will correspond to the in

dices with better discriminating capability.


2.3.4 Clustering Methods: GMM

• Most biometric data cannot be adequately modeled by a single–cluster Gaussian model.

• Gaussian Mixture Model (GMM) provides a more flexible model for describing the distribution of biometric data.– K-means or EM algorithms– Optimal number of clusters


Project-Then-Cluster

• We can adopt more sophisticated strategies such as cluster-the-projectcluster-the-project or project-then-clusterproject-then-cluster.

• Cluster-then-project– A projection aimed at separating two classes,

each modeled by a GMM [404].


PCA

Model Selection

User Interaction + EM + MDL

2-dimensional space (x-space)

EM

(Probabilistic Clustering)

•Cluster initialization

•Clustering in x-space

•Model validation

Gaussian Mixture Model•Clustering in t-space

Page 27

Fig 2.7 An illustration of the project-then-cluster approach. Projection of data from t-space to x-space, then after clustering in the lower-dimension subspace, trace the membership information back to the t-space


Agenda

§ 2.1 Introduction



§ 2.4 Adaptive Classifiers2.4 Adaptive Classifiers


2.4 Adaptive Classifiers

§ 2.4.1 Neural networks

§ 2.4.2 Training strategies

§ 2.4.3 Criteria on classifiers

§ 2.4.4 Availability of training samples


2.4 Adaptive Classifiers

• Statistical approach– Each class is modeled by a normal

distribution– Using prior probabilities, one can compute the

posterior probabilities of each person, conditioned on an observation.


2.4.1 Neural Networks

• A neural work is a simulation of the nervous system that contains neuron unit communicating with one another via axon connections.

• By combining a vast number of simple neurons, it is possible to achieve a sophisticated task.

• Neural networks for biometric applications are discussed in Chapter 5, 6, and 7.


2.4.2 Training Strategies

• Neural networks can learn rules from a collection of examples.

• The ability to learn from examples is a major advantage of neural networks.

• Two types of learning:– Supervised– Unsupervised



• Supervised learning– A neural network is provided with a training

set with labels (the “teacher values”).– The parameters are determined so that the

system can produce answers as close as possible to the teacher values

– e.g., OCR and speaker recognition



• Unsupervised learning– Explore the underlying rules from an

unlabeled training set– Used in the applications where teacher values

are expensive or difficult to obtain


2.4.3 Criteria on Classifiers

• The performance metrics of a learning algorithm– training accuracytraining accuracy: obtained from the training data– generalization accuracygeneralization accuracy: obtained from the testing

data

• There is usually a distinction between training and generalization accuracies.

• High training accuracy does not necessarily yield good generalization accuracy.



• Invariance and noise resilience– Minimize the dependency on environmental

conditions.– Tolerate noise corruption because noise is

inevitable in practical applications.



• Cost-effective system implementation– A cost-effective platform should be

considered.– Emphasis should also be placed on the

issues of system integration.


2.4.4 Availability of Training Samples

• The availability of training data is of critical concern.

• Solutions to the training sample deficiency problem– Conduct an intensive study on the nature of

the selected biometric.– Virtual pattern generation


Intensive study

• An example: fingerprint– The relative positions between various

minutiae are the discriminative features.– The resulting feature vectors could be

separated by simple classifiers.– There is no need to use example to tell the

system which features should be extracted.


Virtual pattern generation

• Create additional training samples– 200 virtual images are generated from one

facial image– Chimerical data


2.5 Visual-Based Biometric Systems


2.6 Audio-Based Biometric Systems

biometric authentication systems

Documents